Notebook 3 of 3

alt text

Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark. The main feature of the Detectron2 is how they enhanced the training time.

The real power of Detectron2 lies in the HUGE amount of pre-trained models available at the Model Zoo. In addition, the Detectron2 is extendible so a lot of custom configuration can be added.

I chose this model because of the hype that was spread in the world about this new state of the art model and in the competition in particular. I am used to the traditional way of training models, where I need to build a train loop to feed the net with samples and targets, choose a loss function and choose an optimizer. This model has a unique training process which includes data preprocessing and mapping, model configuration and more. The loss functions and optimizer are built in but are configurable.

In this notebook I will demonstrate how to use this models, how to train it and the results of the training.

NOTE: submission scores are found on the first notebook

Installation

In [ ]:
!pip install -q -U git+https://github.com/albumentations-team/albumentations
!pip install -q pyyaml==5.1 pycocotools>=2.0.1
!pip install -q detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.6/index.html
In [4]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import gc
import os
import copy
import cv2
import torch
import torchvision
import pycocotools
import detectron2
import random
import itertools

from tqdm.notebook import tqdm
from glob import glob
from detectron2.config import get_cfg
from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor, DefaultTrainer
from detectron2.utils.visualizer import Visualizer, ColorMode
from detectron2.structures import BoxMode
from detectron2.data import datasets, DatasetCatalog, MetadataCatalog, build_detection_train_loader, build_detection_test_loader
from detectron2.data import transforms as T
from detectron2.data import detection_utils as utils
from detectron2.evaluation import COCOEvaluator, verify_results
from detectron2.modeling import GeneralizedRCNNWithTTA
from detectron2.data.transforms import TransformGen
from detectron2.utils.logger import setup_logger
setup_logger()

from fvcore.transforms.transform import TransformList, Transform, NoOpTransform
from contextlib import contextmanager

SEED = 44

def seed_everything(seed):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = True

seed_everything(SEED)

Configurations

In [ ]:
# MODEL_PATH = 'COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml'
MODEL_PATH = 'COCO-Detection/retinanet_R_101_FPN_3x.yaml'

TRAIN_IMAGE_PATH  = '/content/drive/My Drive/CV/Global Wheat Detection/train'
TEST_IMAGE_PATH  = '/content/drive/My Drive/CV/Global Wheat Detection/test'
DATA_PATH = '/content/drive/My Drive/CV/Global Wheat Detection/train.csv'
OUTPUT_PATH ='/content/drive/My Drive/Colab Notebooks/checkpoints/Detectron2'
images_ids = [p.split('/')[-1].split('.')[0] for p in glob(f'{TRAIN_IMAGE_PATH}/*.jpg')]

Import GWD Dataset

In [6]:
def read_csv(path) -> pd.DataFrame:

    df = pd.read_csv(path)
    bboxes = np.stack(df['bbox'].apply(lambda x: np.fromstring(x[1:-1], sep=',')))

    for i, column in enumerate(['x_min', 'y_min', 'width', 'height']):
        df[column] = bboxes[:,i]
    
    df["x_max"] = df.apply(lambda col: col.x_min + col.width, axis=1)
    df["y_max"] = df.apply(lambda col: col.y_min + col.height, axis=1)
    df["area"] = df.apply(lambda col: col.width * col.height, axis=1)
    df["class"] = 1 # 1 for wheat, 0 for background

    df.drop(columns=['source'], inplace=True)
    df.drop(columns=['bbox'], inplace=True)

    return df

train_df = read_csv(DATA_PATH)
train_df.head()
Out[6]:
image_id width height x_min y_min x_max y_max area class
0 b6ab77fd7 56.0 36.0 834.0 222.0 890.0 258.0 2016.0 1
1 b6ab77fd7 130.0 58.0 226.0 548.0 356.0 606.0 7540.0 1
2 b6ab77fd7 74.0 160.0 377.0 504.0 451.0 664.0 11840.0 1
3 b6ab77fd7 109.0 107.0 834.0 95.0 943.0 202.0 11663.0 1
4 b6ab77fd7 124.0 117.0 26.0 144.0 150.0 261.0 14508.0 1

Custom Augmentation

Detectron2 has limited augemntations(Transformation), but they provided the Transform module to make custom augmentations

In [7]:
class CutOut(Transform):
    
    def __init__(self, box_size=25, prob_cutout=0.5):
        super().__init__()
        
        self.box_size = box_size
        self.prob_cutout = prob_cutout       
    def apply_image(self, img):

        if random.random() > self.prob_cutout:
            
            h, w = img.shape[:2]
            num_rand = np.random.randint(10, 20)
            for num_cut in range(num_rand):
                x_rand, y_rand = random.randint(0, w-self.box_size), random.randint(0, h-self.box_size)
                img[x_rand:x_rand+self.box_size, y_rand:y_rand+self.box_size, :] = 0
        
        return np.asarray(img)
    
    def apply_coords(self, coords):
        return coords.astype(np.float32)

Data Pre-processing

Before jumping into the Training phase preprocessing should be done. first, we need to create a function that will convert our dataset into a format that is used by Detectron2 The format can be reviewed at the tutorial that is provided by FAIR themselves, or can be found at their documentations.

I converted every annotation row to a single record with a list of annotations. You might also notice the polygon that is of the exact same shape as the bounding box. This is required for the image segmentation models in Detectron2.

In [ ]:
IMAGE_SIZE = 1024
def custom_dataset(df, dir_image):
    
    dataset_dicts = []
    
    for img_id, img_name in enumerate(images_ids):
        
        record = {}
        image_df = df[df['image_id'] == img_name]
        img_path = f'{dir_image}/{img_name}.jpg'
        
        record['file_name'] = img_path
        record['image_id'] = img_id
        record['height'] = 1024
        record['width'] = 1024
                
        objs = []
        for _, row in image_df.iterrows():
            
            x_min = int(row.x_min)
            y_min = int(row.y_min)
            x_max = int(row.x_max)
            y_max = int(row.y_max)
            
            poly = [(x_min, y_min), (x_max, y_min),
                    (x_max, y_max), (x_min, y_max) ]
            
            poly = list(itertools.chain.from_iterable(poly))
            
            obj = {
               "bbox": [x_min, y_min, x_max, y_max],
               "bbox_mode": BoxMode.XYXY_ABS,
               "segmentation": [poly],
               "category_id": 0,
               "iscrowd" : 0
                
                  }
            
            objs.append(obj)
            
        record['annotations'] = objs
        dataset_dicts.append(record)
        
    return dataset_dicts

Data Mapper

Now, we should implement a mapper function. This function will help us customize the data loader. We this we can add more augmentations and add other configurations for the images.

In [16]:
def custom_mapper(dataset_dict):
    # Implement a mapper, similar to the default DatasetMapper, but with your own customizations
    dataset_dict = copy.deepcopy(dataset_dict)  # it will be modified by code below
    image = utils.read_image(dataset_dict["file_name"], format="BGR")
    transform_list = [
                    T.Resize((512,512)),
                    T.RandomBrightness(0.6, 1.6),
                    T.RandomContrast(0.6, 3),
                    T.RandomSaturation(0.1, 2),
                    T.RandomRotation(angle=[90, 90]),
                    T.RandomFlip(prob=0.4, horizontal=False, vertical=True),
                    T.RandomFlip(prob=0.4, horizontal=True, vertical=False), 
                    CutOut()
                    ]
    image, transforms = T.apply_transform_gens(transform_list, image)
    dataset_dict["image"] = torch.as_tensor(image.transpose(2, 0, 1).astype("float32"))

    annos = [
        utils.transform_instance_annotations(obj, transforms, image.shape[:2])
        for obj in dataset_dict.pop("annotations")
        if obj.get("iscrowd", 0) == 0
    ]
    instances = utils.annotations_to_instances(annos, image.shape[:2])
    dataset_dict["instances"] = utils.filter_empty_instances(instances)
    return dataset_dict


class WheatTrainer(DefaultTrainer):
    
    @classmethod
    def build_train_loader(cls, cfg):
        return build_detection_train_loader(cfg, mapper=custom_mapper)
        

Data Registration

After the customaztions, I had to regitser the dataset so that the model well recognize it as a legitimate dataset.

In [ ]:
def register_dataset(df, dataset_label='wheat_train', image_dir = TRAIN_IMAGE_PATH):
    
    # Register dataset - if dataset is already registered, give it a new name    
    try:
        DatasetCatalog.register(dataset_label, lambda d=df: custom_dataset(df, image_dir))
        MetadataCatalog.get(dataset_label).set(thing_classes = ['wheat'])
    except:
        # Add random int to dataset name to not run into 'Already registered' error
        n = random.randint(1, 1000)
        dataset_label = dataset_label + str(n)
        DatasetCatalog.register(dataset_label, lambda d=df: custom_dataset(df, image_dir))
        MetadataCatalog.get(dataset_label).set(thing_classes = ['wheat'])

    return MetadataCatalog.get(dataset_label), dataset_label

metadata, train_dataset = register_dataset(train_df)

Done! Now we can train the model.


Detectron2 Configurations

Detectron2 has 2 models that can be trained

  1. FasterRCNN
  2. RetinaNet

I chose the retina net because Faster RCNN models were used.

RetinaNet

RetinaNet is a single, unified network composed of a backbone network and two task-specific subnetworks. The backbone is responsible for computing a conv feature map over an entire input image and is an off-the-self convolution network. The first subnet performs classification on the backbones output; the second subnet performs convolution bounding box regression. The retinanet model uses Focal loss as the classification loss, and SmoothL1 as the box regression loss.

Focal Loss

Focal loss is the reshaping of cross entropy loss such that it down-weights the loss assigned to well-classified examples. The novel focal loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training.

After a hours and hours of reviewing the configuration class of the Detectron2's RetinaNet I've finally found how to tune the loss functions parameters, and how to control the learning rates.

SGD

Detectron2 uses SGD as the optimizer for all models

more information about the configurations can be found HERE

In [43]:
cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file(MODEL_PATH))
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(MODEL_PATH)  # f'{OUTPUT_PATH}/model_final.pth' 

# RETINA
cfg.MODEL.RETINANET.NUM_CLASSES = 1 # Configuring the number of outputs

# RetinaNet Loss parameters
cfg.MODEL.RETINANET.FOCAL_LOSS_GAMMA = 4.0
cfg.MODEL.RETINANET.FOCAL_LOSS_ALPHA = 0.25
cfg.MODEL.RETINANET.SMOOTH_L1_LOSS_BETA = 0.5

cfg.DATASETS.TRAIN = (train_dataset,)
cfg.DATASETS.TEST = ()

cfg.DATALOADER.NUM_WORKERS = 4
cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS = False # True if u want to exclude "empty" images

cfg.SOLVER.IMS_PER_BATCH = 2 

# cfg.SOLVER.LR_SCHEDULER_NAME = 'WarmupCosineLR' 
cfg.SOLVER.BASE_LR = 0.00025
cfg.SOLVER.WARMUP_ITERS = 1000
cfg.SOLVER.GAMMA = 0.05
cfg.SOLVER.MAX_ITER = 15000 # 20000 - was used in the competition 
cfg.SOLVER.WEIGHT_DECAY = 1e-3
cfg.SOLVER.MOMENTUM = 0.9
cfg.SOLVER.STEPS = (1000,2000,10000,)
cfg.OUTPUT_DIR = OUTPUT_PATH
os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)

trainer = WheatTrainer(cfg)
Loading config /usr/local/lib/python3.6/dist-packages/detectron2/model_zoo/configs/COCO-Detection/../Base-RetinaNet.yaml with yaml.unsafe_load. Your machine may be at risk if the file contains malicious content.
[08/07 15:00:22 d2.engine.defaults]: Model:
RetinaNet(
  (backbone): FPN(
    (fpn_lateral3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
    (fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (fpn_lateral4): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
    (fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (fpn_lateral5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
    (fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (top_block): LastLevelP6P7(
      (p6): Conv2d(2048, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (p7): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    )
    (bottom_up): ResNet(
      (stem): BasicStem(
        (conv1): Conv2d(
          3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False
          (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
        )
      )
      (res2): Sequential(
        (0): BottleneckBlock(
          (shortcut): Conv2d(
            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv1): Conv2d(
            64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv2): Conv2d(
            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv3): Conv2d(
            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
        )
        (1): BottleneckBlock(
          (conv1): Conv2d(
            256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv2): Conv2d(
            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv3): Conv2d(
            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
        )
        (2): BottleneckBlock(
          (conv1): Conv2d(
            256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv2): Conv2d(
            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv3): Conv2d(
            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
        )
      )
      (res3): Sequential(
        (0): BottleneckBlock(
          (shortcut): Conv2d(
            256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv1): Conv2d(
            256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv2): Conv2d(
            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv3): Conv2d(
            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
        )
        (1): BottleneckBlock(
          (conv1): Conv2d(
            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv2): Conv2d(
            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv3): Conv2d(
            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
        )
        (2): BottleneckBlock(
          (conv1): Conv2d(
            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv2): Conv2d(
            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv3): Conv2d(
            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
        )
        (3): BottleneckBlock(
          (conv1): Conv2d(
            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv2): Conv2d(
            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv3): Conv2d(
            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
        )
      )
      (res4): Sequential(
        (0): BottleneckBlock(
          (shortcut): Conv2d(
            512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
          (conv1): Conv2d(
            512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (1): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (2): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (3): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (4): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (5): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (6): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (7): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (8): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (9): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (10): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (11): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (12): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (13): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (14): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (15): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (16): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (17): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (18): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (19): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (20): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (21): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (22): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
      )
      (res5): Sequential(
        (0): BottleneckBlock(
          (shortcut): Conv2d(
            1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
          )
          (conv1): Conv2d(
            1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv2): Conv2d(
            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv3): Conv2d(
            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
          )
        )
        (1): BottleneckBlock(
          (conv1): Conv2d(
            2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv2): Conv2d(
            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv3): Conv2d(
            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
          )
        )
        (2): BottleneckBlock(
          (conv1): Conv2d(
            2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv2): Conv2d(
            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv3): Conv2d(
            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
          )
        )
      )
    )
  )
  (head): RetinaNetHead(
    (cls_subnet): Sequential(
      (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): ReLU()
      (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (3): ReLU()
      (4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (5): ReLU()
      (6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (7): ReLU()
    )
    (bbox_subnet): Sequential(
      (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): ReLU()
      (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (3): ReLU()
      (4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (5): ReLU()
      (6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (7): ReLU()
    )
    (cls_score): Conv2d(256, 9, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (bbox_pred): Conv2d(256, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  )
  (anchor_generator): DefaultAnchorGenerator(
    (cell_anchors): BufferList()
  )
)
[08/07 15:01:10 d2.data.common]: Serializing 3422 elements to byte tensors and concatenating them all ...
[08/07 15:01:10 d2.data.common]: Serialized dataset takes 9.82 MiB
[08/07 15:01:10 d2.data.build]: Using training sampler TrainingSampler

Data visualization

In [ ]:
train_data_loader = trainer.build_train_loader(cfg)
data_iter = iter(train_data_loader)
batch = next(data_iter)
In [19]:
rows, cols = 1, 2
plt.figure(figsize=(20,20))

for i, per_image in enumerate(batch[:2]):
    
    plt.subplot(rows, cols, i+1)
    
    # Pytorch tensor is in (C, H, W) format
    img = per_image["image"].permute(1, 2, 0).cpu().detach().numpy()
    img = utils.convert_image_to_rgb(img, cfg.INPUT.FORMAT)

    visualizer = Visualizer(img, metadata=metadata, scale=1.0)

    target_fields = per_image["instances"].get_fields()
    labels = None
    vis = visualizer.overlay_instances(
        labels=labels,
        boxes=target_fields.get("gt_boxes", None),
        masks=target_fields.get("gt_masks", None),
        keypoints=target_fields.get("gt_keypoints", None),
    )
    plt.imshow(vis.get_image()[:, :, ::-1])
In [44]:
trainer.resume_or_load(resume=True)
trainer.train()
[08/07 15:01:12 d2.engine.train_loop]: Starting training from iteration 5000
[08/07 15:01:17 d2.utils.events]:  eta: 0:40:01  iter: 5019  total_loss: 0.215  loss_cls: 0.118  loss_box_reg: 0.101  time: 0.2322  data_time: 0.0450  lr: 0.000001  max_mem: 10044M
[08/07 15:01:22 d2.utils.events]:  eta: 0:38:07  iter: 5039  total_loss: 0.135  loss_cls: 0.072  loss_box_reg: 0.066  time: 0.2270  data_time: 0.0133  lr: 0.000001  max_mem: 10044M
[08/07 15:01:27 d2.utils.events]:  eta: 0:38:03  iter: 5059  total_loss: 0.129  loss_cls: 0.065  loss_box_reg: 0.065  time: 0.2278  data_time: 0.0136  lr: 0.000001  max_mem: 10044M
[08/07 15:01:31 d2.utils.events]:  eta: 0:38:01  iter: 5079  total_loss: 0.143  loss_cls: 0.074  loss_box_reg: 0.068  time: 0.2271  data_time: 0.0117  lr: 0.000001  max_mem: 10044M
[08/07 15:01:36 d2.utils.events]:  eta: 0:37:59  iter: 5099  total_loss: 0.150  loss_cls: 0.077  loss_box_reg: 0.072  time: 0.2279  data_time: 0.0115  lr: 0.000001  max_mem: 10044M
[08/07 15:01:40 d2.utils.events]:  eta: 0:37:54  iter: 5119  total_loss: 0.126  loss_cls: 0.067  loss_box_reg: 0.061  time: 0.2271  data_time: 0.0095  lr: 0.000001  max_mem: 10044M
[08/07 15:01:45 d2.utils.events]:  eta: 0:37:50  iter: 5139  total_loss: 0.139  loss_cls: 0.071  loss_box_reg: 0.068  time: 0.2274  data_time: 0.0133  lr: 0.000001  max_mem: 10044M
[08/07 15:01:49 d2.utils.events]:  eta: 0:37:49  iter: 5159  total_loss: 0.146  loss_cls: 0.076  loss_box_reg: 0.070  time: 0.2283  data_time: 0.0126  lr: 0.000001  max_mem: 10044M
[08/07 15:01:54 d2.utils.events]:  eta: 0:37:30  iter: 5179  total_loss: 0.137  loss_cls: 0.073  loss_box_reg: 0.066  time: 0.2275  data_time: 0.0105  lr: 0.000001  max_mem: 10044M
[08/07 15:01:59 d2.utils.events]:  eta: 0:37:36  iter: 5199  total_loss: 0.134  loss_cls: 0.067  loss_box_reg: 0.066  time: 0.2278  data_time: 0.0126  lr: 0.000001  max_mem: 10044M
[08/07 15:02:03 d2.utils.events]:  eta: 0:37:21  iter: 5219  total_loss: 0.133  loss_cls: 0.071  loss_box_reg: 0.064  time: 0.2273  data_time: 0.0123  lr: 0.000001  max_mem: 10044M
[08/07 15:02:08 d2.utils.events]:  eta: 0:37:13  iter: 5239  total_loss: 0.142  loss_cls: 0.072  loss_box_reg: 0.072  time: 0.2273  data_time: 0.0134  lr: 0.000001  max_mem: 10044M
[08/07 15:02:12 d2.utils.events]:  eta: 0:37:08  iter: 5259  total_loss: 0.141  loss_cls: 0.071  loss_box_reg: 0.069  time: 0.2273  data_time: 0.0109  lr: 0.000001  max_mem: 10044M
[08/07 15:02:17 d2.utils.events]:  eta: 0:37:03  iter: 5279  total_loss: 0.138  loss_cls: 0.072  loss_box_reg: 0.068  time: 0.2270  data_time: 0.0131  lr: 0.000001  max_mem: 10044M
[08/07 15:02:21 d2.utils.events]:  eta: 0:36:58  iter: 5299  total_loss: 0.136  loss_cls: 0.066  loss_box_reg: 0.069  time: 0.2270  data_time: 0.0129  lr: 0.000001  max_mem: 10044M
[08/07 15:02:26 d2.utils.events]:  eta: 0:36:54  iter: 5319  total_loss: 0.135  loss_cls: 0.070  loss_box_reg: 0.066  time: 0.2267  data_time: 0.0129  lr: 0.000001  max_mem: 10044M
[08/07 15:02:30 d2.utils.events]:  eta: 0:36:46  iter: 5339  total_loss: 0.135  loss_cls: 0.070  loss_box_reg: 0.059  time: 0.2263  data_time: 0.0129  lr: 0.000001  max_mem: 10044M
[08/07 15:02:35 d2.utils.events]:  eta: 0:36:40  iter: 5359  total_loss: 0.149  loss_cls: 0.075  loss_box_reg: 0.073  time: 0.2261  data_time: 0.0135  lr: 0.000001  max_mem: 10044M
[08/07 15:02:39 d2.utils.events]:  eta: 0:36:36  iter: 5379  total_loss: 0.136  loss_cls: 0.068  loss_box_reg: 0.069  time: 0.2262  data_time: 0.0110  lr: 0.000001  max_mem: 10044M
[08/07 15:02:44 d2.utils.events]:  eta: 0:36:33  iter: 5399  total_loss: 0.147  loss_cls: 0.072  loss_box_reg: 0.074  time: 0.2264  data_time: 0.0122  lr: 0.000001  max_mem: 10044M
[08/07 15:02:48 d2.utils.events]:  eta: 0:36:29  iter: 5419  total_loss: 0.138  loss_cls: 0.068  loss_box_reg: 0.067  time: 0.2266  data_time: 0.0158  lr: 0.000001  max_mem: 10044M
[08/07 15:02:53 d2.utils.events]:  eta: 0:36:22  iter: 5439  total_loss: 0.137  loss_cls: 0.069  loss_box_reg: 0.067  time: 0.2267  data_time: 0.0115  lr: 0.000001  max_mem: 10044M
[08/07 15:02:58 d2.utils.events]:  eta: 0:36:20  iter: 5459  total_loss: 0.140  loss_cls: 0.073  loss_box_reg: 0.066  time: 0.2268  data_time: 0.0126  lr: 0.000001  max_mem: 10044M
[08/07 15:03:02 d2.utils.events]:  eta: 0:36:19  iter: 5479  total_loss: 0.139  loss_cls: 0.075  loss_box_reg: 0.071  time: 0.2270  data_time: 0.0121  lr: 0.000001  max_mem: 10044M
[08/07 15:03:07 d2.utils.events]:  eta: 0:36:12  iter: 5499  total_loss: 0.141  loss_cls: 0.075  loss_box_reg: 0.065  time: 0.2268  data_time: 0.0133  lr: 0.000001  max_mem: 10044M
[08/07 15:03:11 d2.utils.events]:  eta: 0:36:06  iter: 5519  total_loss: 0.128  loss_cls: 0.067  loss_box_reg: 0.061  time: 0.2267  data_time: 0.0131  lr: 0.000001  max_mem: 10044M
[08/07 15:03:16 d2.utils.events]:  eta: 0:36:01  iter: 5539  total_loss: 0.151  loss_cls: 0.079  loss_box_reg: 0.075  time: 0.2268  data_time: 0.0139  lr: 0.000001  max_mem: 10044M
[08/07 15:03:20 d2.utils.events]:  eta: 0:35:57  iter: 5559  total_loss: 0.145  loss_cls: 0.076  loss_box_reg: 0.068  time: 0.2270  data_time: 0.0136  lr: 0.000001  max_mem: 10044M
[08/07 15:03:25 d2.utils.events]:  eta: 0:35:52  iter: 5579  total_loss: 0.130  loss_cls: 0.068  loss_box_reg: 0.065  time: 0.2271  data_time: 0.0120  lr: 0.000001  max_mem: 10044M
[08/07 15:03:30 d2.utils.events]:  eta: 0:35:48  iter: 5599  total_loss: 0.126  loss_cls: 0.063  loss_box_reg: 0.062  time: 0.2271  data_time: 0.0118  lr: 0.000001  max_mem: 10044M
[08/07 15:03:34 d2.utils.events]:  eta: 0:35:41  iter: 5619  total_loss: 0.127  loss_cls: 0.066  loss_box_reg: 0.064  time: 0.2267  data_time: 0.0134  lr: 0.000001  max_mem: 10044M
[08/07 15:03:38 d2.utils.events]:  eta: 0:35:37  iter: 5639  total_loss: 0.143  loss_cls: 0.075  loss_box_reg: 0.070  time: 0.2265  data_time: 0.0114  lr: 0.000001  max_mem: 10044M
[08/07 15:03:43 d2.utils.events]:  eta: 0:35:20  iter: 5659  total_loss: 0.146  loss_cls: 0.076  loss_box_reg: 0.069  time: 0.2264  data_time: 0.0143  lr: 0.000001  max_mem: 10044M
[08/07 15:03:47 d2.utils.events]:  eta: 0:35:16  iter: 5679  total_loss: 0.133  loss_cls: 0.070  loss_box_reg: 0.062  time: 0.2262  data_time: 0.0115  lr: 0.000001  max_mem: 10044M
[08/07 15:03:52 d2.utils.events]:  eta: 0:35:07  iter: 5699  total_loss: 0.136  loss_cls: 0.071  loss_box_reg: 0.065  time: 0.2263  data_time: 0.0138  lr: 0.000001  max_mem: 10044M
[08/07 15:03:56 d2.utils.events]:  eta: 0:35:02  iter: 5719  total_loss: 0.145  loss_cls: 0.071  loss_box_reg: 0.069  time: 0.2263  data_time: 0.0127  lr: 0.000001  max_mem: 10044M
[08/07 15:04:01 d2.utils.events]:  eta: 0:34:55  iter: 5739  total_loss: 0.155  loss_cls: 0.079  loss_box_reg: 0.076  time: 0.2260  data_time: 0.0127  lr: 0.000001  max_mem: 10044M
[08/07 15:04:05 d2.utils.events]:  eta: 0:34:49  iter: 5759  total_loss: 0.143  loss_cls: 0.076  loss_box_reg: 0.069  time: 0.2258  data_time: 0.0096  lr: 0.000001  max_mem: 10044M
[08/07 15:04:10 d2.utils.events]:  eta: 0:34:44  iter: 5779  total_loss: 0.144  loss_cls: 0.074  loss_box_reg: 0.070  time: 0.2257  data_time: 0.0119  lr: 0.000001  max_mem: 10044M
[08/07 15:04:14 d2.utils.events]:  eta: 0:34:39  iter: 5799  total_loss: 0.127  loss_cls: 0.066  loss_box_reg: 0.060  time: 0.2256  data_time: 0.0101  lr: 0.000001  max_mem: 10044M
[08/07 15:04:18 d2.utils.events]:  eta: 0:34:34  iter: 5819  total_loss: 0.143  loss_cls: 0.076  loss_box_reg: 0.069  time: 0.2255  data_time: 0.0094  lr: 0.000001  max_mem: 10044M
[08/07 15:04:23 d2.utils.events]:  eta: 0:34:30  iter: 5839  total_loss: 0.131  loss_cls: 0.067  loss_box_reg: 0.065  time: 0.2256  data_time: 0.0123  lr: 0.000001  max_mem: 10044M
[08/07 15:04:27 d2.utils.events]:  eta: 0:34:25  iter: 5859  total_loss: 0.144  loss_cls: 0.075  loss_box_reg: 0.071  time: 0.2255  data_time: 0.0123  lr: 0.000001  max_mem: 10044M
[08/07 15:04:32 d2.utils.events]:  eta: 0:34:20  iter: 5879  total_loss: 0.143  loss_cls: 0.074  loss_box_reg: 0.070  time: 0.2254  data_time: 0.0121  lr: 0.000001  max_mem: 10044M
[08/07 15:04:37 d2.utils.events]:  eta: 0:34:15  iter: 5899  total_loss: 0.137  loss_cls: 0.067  loss_box_reg: 0.067  time: 0.2255  data_time: 0.0133  lr: 0.000001  max_mem: 10044M
[08/07 15:04:41 d2.utils.events]:  eta: 0:34:12  iter: 5919  total_loss: 0.141  loss_cls: 0.073  loss_box_reg: 0.069  time: 0.2258  data_time: 0.0131  lr: 0.000001  max_mem: 10044M
[08/07 15:04:46 d2.utils.events]:  eta: 0:34:09  iter: 5939  total_loss: 0.123  loss_cls: 0.063  loss_box_reg: 0.060  time: 0.2261  data_time: 0.0149  lr: 0.000001  max_mem: 10044M
[08/07 15:04:51 d2.utils.events]:  eta: 0:34:07  iter: 5959  total_loss: 0.138  loss_cls: 0.074  loss_box_reg: 0.069  time: 0.2261  data_time: 0.0137  lr: 0.000001  max_mem: 10044M
[08/07 15:04:55 d2.utils.events]:  eta: 0:34:03  iter: 5979  total_loss: 0.137  loss_cls: 0.074  loss_box_reg: 0.066  time: 0.2262  data_time: 0.0121  lr: 0.000001  max_mem: 10044M
[08/07 15:05:00 d2.utils.events]:  eta: 0:33:59  iter: 5999  total_loss: 0.158  loss_cls: 0.080  loss_box_reg: 0.077  time: 0.2264  data_time: 0.0132  lr: 0.000001  max_mem: 10044M
[08/07 15:05:05 d2.utils.events]:  eta: 0:33:55  iter: 6019  total_loss: 0.144  loss_cls: 0.075  loss_box_reg: 0.068  time: 0.2266  data_time: 0.0135  lr: 0.000001  max_mem: 10044M
[08/07 15:05:09 d2.utils.events]:  eta: 0:33:58  iter: 6039  total_loss: 0.119  loss_cls: 0.064  loss_box_reg: 0.057  time: 0.2268  data_time: 0.0131  lr: 0.000001  max_mem: 10044M
[08/07 15:05:14 d2.utils.events]:  eta: 0:33:51  iter: 6059  total_loss: 0.137  loss_cls: 0.068  loss_box_reg: 0.069  time: 0.2267  data_time: 0.0136  lr: 0.000001  max_mem: 10044M
[08/07 15:05:19 d2.utils.events]:  eta: 0:33:49  iter: 6079  total_loss: 0.139  loss_cls: 0.071  loss_box_reg: 0.066  time: 0.2268  data_time: 0.0140  lr: 0.000001  max_mem: 10044M
[08/07 15:05:23 d2.utils.events]:  eta: 0:33:42  iter: 6099  total_loss: 0.127  loss_cls: 0.065  loss_box_reg: 0.063  time: 0.2269  data_time: 0.0152  lr: 0.000001  max_mem: 10044M
[08/07 15:05:28 d2.utils.events]:  eta: 0:33:40  iter: 6119  total_loss: 0.133  loss_cls: 0.072  loss_box_reg: 0.062  time: 0.2274  data_time: 0.0149  lr: 0.000001  max_mem: 10044M
[08/07 15:05:33 d2.utils.events]:  eta: 0:33:38  iter: 6139  total_loss: 0.137  loss_cls: 0.071  loss_box_reg: 0.068  time: 0.2275  data_time: 0.0127  lr: 0.000001  max_mem: 10044M
[08/07 15:05:38 d2.utils.events]:  eta: 0:33:35  iter: 6159  total_loss: 0.131  loss_cls: 0.065  loss_box_reg: 0.062  time: 0.2276  data_time: 0.0134  lr: 0.000001  max_mem: 10044M
[08/07 15:05:42 d2.utils.events]:  eta: 0:33:32  iter: 6179  total_loss: 0.143  loss_cls: 0.072  loss_box_reg: 0.071  time: 0.2277  data_time: 0.0150  lr: 0.000001  max_mem: 10044M
[08/07 15:05:47 d2.utils.events]:  eta: 0:33:29  iter: 6199  total_loss: 0.141  loss_cls: 0.073  loss_box_reg: 0.066  time: 0.2278  data_time: 0.0146  lr: 0.000001  max_mem: 10044M
[08/07 15:05:52 d2.utils.events]:  eta: 0:33:27  iter: 6219  total_loss: 0.143  loss_cls: 0.073  loss_box_reg: 0.067  time: 0.2280  data_time: 0.0127  lr: 0.000001  max_mem: 10044M
[08/07 15:05:57 d2.utils.events]:  eta: 0:33:25  iter: 6239  total_loss: 0.132  loss_cls: 0.070  loss_box_reg: 0.063  time: 0.2281  data_time: 0.0140  lr: 0.000001  max_mem: 10044M
[08/07 15:06:01 d2.utils.events]:  eta: 0:33:24  iter: 6259  total_loss: 0.147  loss_cls: 0.079  loss_box_reg: 0.070  time: 0.2282  data_time: 0.0125  lr: 0.000001  max_mem: 10044M
[08/07 15:06:06 d2.utils.events]:  eta: 0:33:18  iter: 6279  total_loss: 0.135  loss_cls: 0.072  loss_box_reg: 0.065  time: 0.2282  data_time: 0.0111  lr: 0.000001  max_mem: 10044M
[08/07 15:06:10 d2.utils.events]:  eta: 0:33:13  iter: 6299  total_loss: 0.143  loss_cls: 0.073  loss_box_reg: 0.068  time: 0.2281  data_time: 0.0127  lr: 0.000001  max_mem: 10044M
[08/07 15:06:15 d2.utils.events]:  eta: 0:33:13  iter: 6319  total_loss: 0.137  loss_cls: 0.068  loss_box_reg: 0.070  time: 0.2282  data_time: 0.0133  lr: 0.000001  max_mem: 10044M
[08/07 15:06:20 d2.utils.events]:  eta: 0:33:09  iter: 6339  total_loss: 0.145  loss_cls: 0.077  loss_box_reg: 0.066  time: 0.2282  data_time: 0.0144  lr: 0.000001  max_mem: 10044M
[08/07 15:06:24 d2.utils.events]:  eta: 0:33:09  iter: 6359  total_loss: 0.141  loss_cls: 0.075  loss_box_reg: 0.068  time: 0.2283  data_time: 0.0145  lr: 0.000001  max_mem: 10044M
[08/07 15:06:29 d2.utils.events]:  eta: 0:33:05  iter: 6379  total_loss: 0.141  loss_cls: 0.070  loss_box_reg: 0.072  time: 0.2285  data_time: 0.0169  lr: 0.000001  max_mem: 10044M
[08/07 15:06:34 d2.utils.events]:  eta: 0:33:01  iter: 6399  total_loss: 0.135  loss_cls: 0.067  loss_box_reg: 0.067  time: 0.2287  data_time: 0.0153  lr: 0.000001  max_mem: 10044M
[08/07 15:06:39 d2.utils.events]:  eta: 0:32:58  iter: 6419  total_loss: 0.136  loss_cls: 0.071  loss_box_reg: 0.065  time: 0.2289  data_time: 0.0144  lr: 0.000001  max_mem: 10044M
[08/07 15:06:44 d2.utils.events]:  eta: 0:32:55  iter: 6439  total_loss: 0.144  loss_cls: 0.071  loss_box_reg: 0.070  time: 0.2290  data_time: 0.0136  lr: 0.000001  max_mem: 10044M
[08/07 15:06:49 d2.utils.events]:  eta: 0:32:50  iter: 6459  total_loss: 0.130  loss_cls: 0.066  loss_box_reg: 0.066  time: 0.2291  data_time: 0.0136  lr: 0.000001  max_mem: 10044M
[08/07 15:06:53 d2.utils.events]:  eta: 0:32:44  iter: 6479  total_loss: 0.145  loss_cls: 0.074  loss_box_reg: 0.070  time: 0.2292  data_time: 0.0133  lr: 0.000001  max_mem: 10044M
[08/07 15:06:58 d2.utils.events]:  eta: 0:32:42  iter: 6499  total_loss: 0.133  loss_cls: 0.068  loss_box_reg: 0.068  time: 0.2293  data_time: 0.0168  lr: 0.000001  max_mem: 10044M
[08/07 15:07:03 d2.utils.events]:  eta: 0:32:41  iter: 6519  total_loss: 0.137  loss_cls: 0.071  loss_box_reg: 0.069  time: 0.2293  data_time: 0.0118  lr: 0.000001  max_mem: 10044M
[08/07 15:07:07 d2.utils.events]:  eta: 0:32:37  iter: 6539  total_loss: 0.123  loss_cls: 0.065  loss_box_reg: 0.058  time: 0.2294  data_time: 0.0156  lr: 0.000001  max_mem: 10044M
[08/07 15:07:12 d2.utils.events]:  eta: 0:32:34  iter: 6559  total_loss: 0.137  loss_cls: 0.069  loss_box_reg: 0.068  time: 0.2296  data_time: 0.0144  lr: 0.000001  max_mem: 10044M
[08/07 15:07:17 d2.utils.events]:  eta: 0:32:30  iter: 6579  total_loss: 0.145  loss_cls: 0.075  loss_box_reg: 0.068  time: 0.2296  data_time: 0.0153  lr: 0.000001  max_mem: 10044M
[08/07 15:07:22 d2.utils.events]:  eta: 0:32:25  iter: 6599  total_loss: 0.128  loss_cls: 0.066  loss_box_reg: 0.065  time: 0.2297  data_time: 0.0140  lr: 0.000001  max_mem: 10044M
[08/07 15:07:26 d2.utils.events]:  eta: 0:32:24  iter: 6619  total_loss: 0.150  loss_cls: 0.081  loss_box_reg: 0.072  time: 0.2297  data_time: 0.0130  lr: 0.000001  max_mem: 10044M
[08/07 15:07:31 d2.utils.events]:  eta: 0:32:20  iter: 6639  total_loss: 0.149  loss_cls: 0.079  loss_box_reg: 0.068  time: 0.2298  data_time: 0.0125  lr: 0.000001  max_mem: 10044M
[08/07 15:07:36 d2.utils.events]:  eta: 0:32:15  iter: 6659  total_loss: 0.141  loss_cls: 0.069  loss_box_reg: 0.069  time: 0.2298  data_time: 0.0106  lr: 0.000001  max_mem: 10044M
[08/07 15:07:40 d2.utils.events]:  eta: 0:32:12  iter: 6679  total_loss: 0.126  loss_cls: 0.069  loss_box_reg: 0.057  time: 0.2298  data_time: 0.0153  lr: 0.000001  max_mem: 10044M
[08/07 15:07:45 d2.utils.events]:  eta: 0:32:08  iter: 6699  total_loss: 0.116  loss_cls: 0.059  loss_box_reg: 0.057  time: 0.2297  data_time: 0.0134  lr: 0.000001  max_mem: 10044M
[08/07 15:07:50 d2.utils.events]:  eta: 0:32:05  iter: 6719  total_loss: 0.142  loss_cls: 0.076  loss_box_reg: 0.067  time: 0.2298  data_time: 0.0136  lr: 0.000001  max_mem: 10044M
[08/07 15:07:54 d2.utils.events]:  eta: 0:32:05  iter: 6739  total_loss: 0.149  loss_cls: 0.079  loss_box_reg: 0.069  time: 0.2299  data_time: 0.0105  lr: 0.000001  max_mem: 10044M
[08/07 15:07:59 d2.utils.events]:  eta: 0:32:02  iter: 6759  total_loss: 0.150  loss_cls: 0.073  loss_box_reg: 0.072  time: 0.2300  data_time: 0.0128  lr: 0.000001  max_mem: 10044M
[08/07 15:08:04 d2.utils.events]:  eta: 0:31:59  iter: 6779  total_loss: 0.140  loss_cls: 0.070  loss_box_reg: 0.074  time: 0.2301  data_time: 0.0139  lr: 0.000001  max_mem: 10044M
[08/07 15:08:09 d2.utils.events]:  eta: 0:31:56  iter: 6799  total_loss: 0.121  loss_cls: 0.065  loss_box_reg: 0.056  time: 0.2302  data_time: 0.0149  lr: 0.000001  max_mem: 10044M
[08/07 15:08:13 d2.utils.events]:  eta: 0:31:55  iter: 6819  total_loss: 0.138  loss_cls: 0.073  loss_box_reg: 0.066  time: 0.2302  data_time: 0.0165  lr: 0.000001  max_mem: 10044M
[08/07 15:08:18 d2.utils.events]:  eta: 0:31:51  iter: 6839  total_loss: 0.126  loss_cls: 0.066  loss_box_reg: 0.059  time: 0.2304  data_time: 0.0156  lr: 0.000001  max_mem: 10044M
[08/07 15:08:23 d2.utils.events]:  eta: 0:31:51  iter: 6859  total_loss: 0.146  loss_cls: 0.075  loss_box_reg: 0.072  time: 0.2305  data_time: 0.0138  lr: 0.000001  max_mem: 10044M
[08/07 15:08:28 d2.utils.events]:  eta: 0:31:47  iter: 6879  total_loss: 0.131  loss_cls: 0.068  loss_box_reg: 0.067  time: 0.2305  data_time: 0.0148  lr: 0.000001  max_mem: 10044M
[08/07 15:08:32 d2.utils.events]:  eta: 0:31:43  iter: 6899  total_loss: 0.124  loss_cls: 0.065  loss_box_reg: 0.060  time: 0.2305  data_time: 0.0140  lr: 0.000001  max_mem: 10044M
[08/07 15:08:37 d2.utils.events]:  eta: 0:31:38  iter: 6919  total_loss: 0.137  loss_cls: 0.069  loss_box_reg: 0.066  time: 0.2305  data_time: 0.0173  lr: 0.000001  max_mem: 10044M
[08/07 15:08:42 d2.utils.events]:  eta: 0:31:32  iter: 6939  total_loss: 0.153  loss_cls: 0.080  loss_box_reg: 0.076  time: 0.2306  data_time: 0.0138  lr: 0.000001  max_mem: 10044M
[08/07 15:08:46 d2.utils.events]:  eta: 0:31:23  iter: 6959  total_loss: 0.145  loss_cls: 0.074  loss_box_reg: 0.070  time: 0.2305  data_time: 0.0150  lr: 0.000001  max_mem: 10044M
[08/07 15:08:51 d2.utils.events]:  eta: 0:31:22  iter: 6979  total_loss: 0.142  loss_cls: 0.075  loss_box_reg: 0.068  time: 0.2306  data_time: 0.0162  lr: 0.000001  max_mem: 10044M
[08/07 15:08:56 d2.utils.events]:  eta: 0:31:18  iter: 6999  total_loss: 0.134  loss_cls: 0.070  loss_box_reg: 0.064  time: 0.2307  data_time: 0.0141  lr: 0.000001  max_mem: 10044M
[08/07 15:09:00 d2.utils.events]:  eta: 0:31:11  iter: 7019  total_loss: 0.133  loss_cls: 0.067  loss_box_reg: 0.065  time: 0.2306  data_time: 0.0147  lr: 0.000001  max_mem: 10044M
[08/07 15:09:05 d2.utils.events]:  eta: 0:31:06  iter: 7039  total_loss: 0.141  loss_cls: 0.077  loss_box_reg: 0.066  time: 0.2307  data_time: 0.0124  lr: 0.000001  max_mem: 10044M
[08/07 15:09:10 d2.utils.events]:  eta: 0:31:03  iter: 7059  total_loss: 0.142  loss_cls: 0.073  loss_box_reg: 0.068  time: 0.2307  data_time: 0.0139  lr: 0.000001  max_mem: 10044M
[08/07 15:09:14 d2.utils.events]:  eta: 0:30:54  iter: 7079  total_loss: 0.131  loss_cls: 0.069  loss_box_reg: 0.065  time: 0.2307  data_time: 0.0113  lr: 0.000001  max_mem: 10044M
[08/07 15:09:19 d2.utils.events]:  eta: 0:30:49  iter: 7099  total_loss: 0.129  loss_cls: 0.067  loss_box_reg: 0.062  time: 0.2307  data_time: 0.0113  lr: 0.000001  max_mem: 10044M
[08/07 15:09:24 d2.utils.events]:  eta: 0:30:43  iter: 7119  total_loss: 0.155  loss_cls: 0.077  loss_box_reg: 0.076  time: 0.2307  data_time: 0.0112  lr: 0.000001  max_mem: 10044M
[08/07 15:09:28 d2.utils.events]:  eta: 0:30:36  iter: 7139  total_loss: 0.140  loss_cls: 0.071  loss_box_reg: 0.068  time: 0.2306  data_time: 0.0128  lr: 0.000001  max_mem: 10044M
[08/07 15:09:33 d2.utils.events]:  eta: 0:30:29  iter: 7159  total_loss: 0.144  loss_cls: 0.073  loss_box_reg: 0.072  time: 0.2305  data_time: 0.0139  lr: 0.000001  max_mem: 10044M
[08/07 15:09:37 d2.utils.events]:  eta: 0:30:24  iter: 7179  total_loss: 0.140  loss_cls: 0.070  loss_box_reg: 0.067  time: 0.2305  data_time: 0.0120  lr: 0.000001  max_mem: 10044M
[08/07 15:09:42 d2.utils.events]:  eta: 0:30:19  iter: 7199  total_loss: 0.134  loss_cls: 0.070  loss_box_reg: 0.064  time: 0.2305  data_time: 0.0126  lr: 0.000001  max_mem: 10044M
[08/07 15:09:46 d2.utils.events]:  eta: 0:30:15  iter: 7219  total_loss: 0.140  loss_cls: 0.070  loss_box_reg: 0.070  time: 0.2305  data_time: 0.0133  lr: 0.000001  max_mem: 10044M
[08/07 15:09:51 d2.utils.events]:  eta: 0:30:09  iter: 7239  total_loss: 0.146  loss_cls: 0.072  loss_box_reg: 0.074  time: 0.2305  data_time: 0.0112  lr: 0.000001  max_mem: 10044M
[08/07 15:09:56 d2.utils.events]:  eta: 0:30:02  iter: 7259  total_loss: 0.135  loss_cls: 0.067  loss_box_reg: 0.067  time: 0.2304  data_time: 0.0133  lr: 0.000001  max_mem: 10044M
[08/07 15:10:00 d2.utils.events]:  eta: 0:29:59  iter: 7279  total_loss: 0.143  loss_cls: 0.075  loss_box_reg: 0.068  time: 0.2305  data_time: 0.0158  lr: 0.000001  max_mem: 10044M
[08/07 15:10:05 d2.utils.events]:  eta: 0:29:55  iter: 7299  total_loss: 0.139  loss_cls: 0.074  loss_box_reg: 0.068  time: 0.2305  data_time: 0.0129  lr: 0.000001  max_mem: 10044M
[08/07 15:10:10 d2.utils.events]:  eta: 0:29:51  iter: 7319  total_loss: 0.138  loss_cls: 0.070  loss_box_reg: 0.067  time: 0.2305  data_time: 0.0129  lr: 0.000001  max_mem: 10044M
[08/07 15:10:14 d2.utils.events]:  eta: 0:29:47  iter: 7339  total_loss: 0.138  loss_cls: 0.070  loss_box_reg: 0.067  time: 0.2305  data_time: 0.0140  lr: 0.000001  max_mem: 10044M
[08/07 15:10:19 d2.utils.events]:  eta: 0:29:39  iter: 7359  total_loss: 0.119  loss_cls: 0.061  loss_box_reg: 0.058  time: 0.2304  data_time: 0.0115  lr: 0.000001  max_mem: 10044M
[08/07 15:10:23 d2.utils.events]:  eta: 0:29:34  iter: 7379  total_loss: 0.151  loss_cls: 0.073  loss_box_reg: 0.076  time: 0.2304  data_time: 0.0131  lr: 0.000001  max_mem: 10044M
[08/07 15:10:28 d2.utils.events]:  eta: 0:29:29  iter: 7399  total_loss: 0.135  loss_cls: 0.064  loss_box_reg: 0.065  time: 0.2304  data_time: 0.0109  lr: 0.000001  max_mem: 10044M
[08/07 15:10:32 d2.utils.events]:  eta: 0:29:25  iter: 7419  total_loss: 0.157  loss_cls: 0.081  loss_box_reg: 0.077  time: 0.2304  data_time: 0.0123  lr: 0.000001  max_mem: 10044M
[08/07 15:10:37 d2.utils.events]:  eta: 0:29:20  iter: 7439  total_loss: 0.138  loss_cls: 0.074  loss_box_reg: 0.064  time: 0.2304  data_time: 0.0148  lr: 0.000001  max_mem: 10044M
[08/07 15:10:42 d2.utils.events]:  eta: 0:29:15  iter: 7459  total_loss: 0.144  loss_cls: 0.074  loss_box_reg: 0.069  time: 0.2303  data_time: 0.0135  lr: 0.000001  max_mem: 10044M
[08/07 15:10:46 d2.utils.events]:  eta: 0:29:09  iter: 7479  total_loss: 0.148  loss_cls: 0.073  loss_box_reg: 0.073  time: 0.2303  data_time: 0.0120  lr: 0.000001  max_mem: 10044M
[08/07 15:10:51 d2.utils.events]:  eta: 0:29:02  iter: 7499  total_loss: 0.139  loss_cls: 0.073  loss_box_reg: 0.067  time: 0.2303  data_time: 0.0125  lr: 0.000001  max_mem: 10044M
[08/07 15:10:55 d2.utils.events]:  eta: 0:28:55  iter: 7519  total_loss: 0.155  loss_cls: 0.072  loss_box_reg: 0.076  time: 0.2303  data_time: 0.0128  lr: 0.000001  max_mem: 10044M
[08/07 15:11:00 d2.utils.events]:  eta: 0:28:48  iter: 7539  total_loss: 0.138  loss_cls: 0.074  loss_box_reg: 0.063  time: 0.2302  data_time: 0.0134  lr: 0.000001  max_mem: 10044M
[08/07 15:11:04 d2.utils.events]:  eta: 0:28:37  iter: 7559  total_loss: 0.146  loss_cls: 0.075  loss_box_reg: 0.070  time: 0.2302  data_time: 0.0138  lr: 0.000001  max_mem: 10044M
[08/07 15:11:09 d2.utils.events]:  eta: 0:28:32  iter: 7579  total_loss: 0.152  loss_cls: 0.076  loss_box_reg: 0.074  time: 0.2302  data_time: 0.0122  lr: 0.000001  max_mem: 10044M
[08/07 15:11:13 d2.utils.events]:  eta: 0:28:25  iter: 7599  total_loss: 0.136  loss_cls: 0.069  loss_box_reg: 0.066  time: 0.2301  data_time: 0.0151  lr: 0.000001  max_mem: 10044M
[08/07 15:11:18 d2.utils.events]:  eta: 0:28:19  iter: 7619  total_loss: 0.134  loss_cls: 0.071  loss_box_reg: 0.064  time: 0.2301  data_time: 0.0129  lr: 0.000001  max_mem: 10044M
[08/07 15:11:23 d2.utils.events]:  eta: 0:28:16  iter: 7639  total_loss: 0.129  loss_cls: 0.067  loss_box_reg: 0.063  time: 0.2301  data_time: 0.0154  lr: 0.000001  max_mem: 10044M
[08/07 15:11:27 d2.utils.events]:  eta: 0:28:12  iter: 7659  total_loss: 0.142  loss_cls: 0.074  loss_box_reg: 0.067  time: 0.2301  data_time: 0.0121  lr: 0.000001  max_mem: 10044M
[08/07 15:11:32 d2.utils.events]:  eta: 0:28:06  iter: 7679  total_loss: 0.134  loss_cls: 0.067  loss_box_reg: 0.064  time: 0.2300  data_time: 0.0112  lr: 0.000001  max_mem: 10044M
[08/07 15:11:36 d2.utils.events]:  eta: 0:28:03  iter: 7699  total_loss: 0.140  loss_cls: 0.073  loss_box_reg: 0.067  time: 0.2300  data_time: 0.0127  lr: 0.000001  max_mem: 10044M
[08/07 15:11:41 d2.utils.events]:  eta: 0:27:57  iter: 7719  total_loss: 0.120  loss_cls: 0.062  loss_box_reg: 0.055  time: 0.2300  data_time: 0.0145  lr: 0.000001  max_mem: 10044M
[08/07 15:11:45 d2.utils.events]:  eta: 0:27:47  iter: 7739  total_loss: 0.165  loss_cls: 0.082  loss_box_reg: 0.081  time: 0.2300  data_time: 0.0109  lr: 0.000001  max_mem: 10044M
[08/07 15:11:50 d2.utils.events]:  eta: 0:27:42  iter: 7759  total_loss: 0.151  loss_cls: 0.077  loss_box_reg: 0.073  time: 0.2299  data_time: 0.0155  lr: 0.000001  max_mem: 10044M
[08/07 15:11:54 d2.utils.events]:  eta: 0:27:34  iter: 7779  total_loss: 0.137  loss_cls: 0.072  loss_box_reg: 0.066  time: 0.2299  data_time: 0.0129  lr: 0.000001  max_mem: 10044M
[08/07 15:11:59 d2.utils.events]:  eta: 0:27:29  iter: 7799  total_loss: 0.117  loss_cls: 0.062  loss_box_reg: 0.056  time: 0.2299  data_time: 0.0137  lr: 0.000001  max_mem: 10044M
[08/07 15:12:03 d2.utils.events]:  eta: 0:27:24  iter: 7819  total_loss: 0.149  loss_cls: 0.075  loss_box_reg: 0.070  time: 0.2299  data_time: 0.0134  lr: 0.000001  max_mem: 10044M
[08/07 15:12:08 d2.utils.events]:  eta: 0:27:19  iter: 7839  total_loss: 0.139  loss_cls: 0.071  loss_box_reg: 0.067  time: 0.2298  data_time: 0.0147  lr: 0.000001  max_mem: 10044M
[08/07 15:12:13 d2.utils.events]:  eta: 0:27:14  iter: 7859  total_loss: 0.142  loss_cls: 0.075  loss_box_reg: 0.067  time: 0.2299  data_time: 0.0112  lr: 0.000001  max_mem: 10044M
[08/07 15:12:17 d2.utils.events]:  eta: 0:27:09  iter: 7879  total_loss: 0.140  loss_cls: 0.074  loss_box_reg: 0.065  time: 0.2298  data_time: 0.0127  lr: 0.000001  max_mem: 10044M
[08/07 15:12:22 d2.utils.events]:  eta: 0:27:04  iter: 7899  total_loss: 0.144  loss_cls: 0.072  loss_box_reg: 0.071  time: 0.2298  data_time: 0.0126  lr: 0.000001  max_mem: 10044M
[08/07 15:12:26 d2.utils.events]:  eta: 0:26:59  iter: 7919  total_loss: 0.143  loss_cls: 0.075  loss_box_reg: 0.071  time: 0.2298  data_time: 0.0123  lr: 0.000001  max_mem: 10044M
[08/07 15:12:31 d2.utils.events]:  eta: 0:26:55  iter: 7939  total_loss: 0.142  loss_cls: 0.071  loss_box_reg: 0.069  time: 0.2298  data_time: 0.0108  lr: 0.000001  max_mem: 10044M
[08/07 15:12:35 d2.utils.events]:  eta: 0:26:49  iter: 7959  total_loss: 0.136  loss_cls: 0.069  loss_box_reg: 0.066  time: 0.2297  data_time: 0.0122  lr: 0.000001  max_mem: 10044M
[08/07 15:12:40 d2.utils.events]:  eta: 0:26:42  iter: 7979  total_loss: 0.133  loss_cls: 0.072  loss_box_reg: 0.067  time: 0.2296  data_time: 0.0148  lr: 0.000001  max_mem: 10044M
[08/07 15:12:44 d2.utils.events]:  eta: 0:26:39  iter: 7999  total_loss: 0.146  loss_cls: 0.073  loss_box_reg: 0.072  time: 0.2296  data_time: 0.0121  lr: 0.000001  max_mem: 10044M
[08/07 15:12:49 d2.utils.events]:  eta: 0:26:34  iter: 8019  total_loss: 0.146  loss_cls: 0.075  loss_box_reg: 0.070  time: 0.2296  data_time: 0.0119  lr: 0.000001  max_mem: 10044M
[08/07 15:12:53 d2.utils.events]:  eta: 0:26:29  iter: 8039  total_loss: 0.122  loss_cls: 0.065  loss_box_reg: 0.057  time: 0.2296  data_time: 0.0127  lr: 0.000001  max_mem: 10044M
[08/07 15:12:58 d2.utils.events]:  eta: 0:26:24  iter: 8059  total_loss: 0.128  loss_cls: 0.068  loss_box_reg: 0.061  time: 0.2296  data_time: 0.0134  lr: 0.000001  max_mem: 10044M
[08/07 15:13:03 d2.utils.events]:  eta: 0:26:19  iter: 8079  total_loss: 0.141  loss_cls: 0.071  loss_box_reg: 0.067  time: 0.2296  data_time: 0.0106  lr: 0.000001  max_mem: 10044M
[08/07 15:13:07 d2.utils.events]:  eta: 0:26:15  iter: 8099  total_loss: 0.150  loss_cls: 0.077  loss_box_reg: 0.071  time: 0.2295  data_time: 0.0130  lr: 0.000001  max_mem: 10044M
[08/07 15:13:12 d2.utils.events]:  eta: 0:26:10  iter: 8119  total_loss: 0.133  loss_cls: 0.071  loss_box_reg: 0.061  time: 0.2295  data_time: 0.0127  lr: 0.000001  max_mem: 10044M
[08/07 15:13:16 d2.utils.events]:  eta: 0:26:06  iter: 8139  total_loss: 0.140  loss_cls: 0.073  loss_box_reg: 0.065  time: 0.2295  data_time: 0.0147  lr: 0.000001  max_mem: 10044M
[08/07 15:13:21 d2.utils.events]:  eta: 0:26:01  iter: 8159  total_loss: 0.142  loss_cls: 0.074  loss_box_reg: 0.068  time: 0.2295  data_time: 0.0129  lr: 0.000001  max_mem: 10044M
[08/07 15:13:25 d2.utils.events]:  eta: 0:25:56  iter: 8179  total_loss: 0.136  loss_cls: 0.070  loss_box_reg: 0.068  time: 0.2295  data_time: 0.0103  lr: 0.000001  max_mem: 10044M
[08/07 15:13:30 d2.utils.events]:  eta: 0:25:52  iter: 8199  total_loss: 0.143  loss_cls: 0.076  loss_box_reg: 0.066  time: 0.2295  data_time: 0.0103  lr: 0.000001  max_mem: 10044M
[08/07 15:13:34 d2.utils.events]:  eta: 0:25:46  iter: 8219  total_loss: 0.131  loss_cls: 0.070  loss_box_reg: 0.062  time: 0.2294  data_time: 0.0116  lr: 0.000001  max_mem: 10044M
[08/07 15:13:39 d2.utils.events]:  eta: 0:25:42  iter: 8239  total_loss: 0.135  loss_cls: 0.067  loss_box_reg: 0.066  time: 0.2294  data_time: 0.0121  lr: 0.000001  max_mem: 10044M
[08/07 15:13:43 d2.utils.events]:  eta: 0:25:37  iter: 8259  total_loss: 0.136  loss_cls: 0.068  loss_box_reg: 0.072  time: 0.2294  data_time: 0.0129  lr: 0.000001  max_mem: 10044M
[08/07 15:13:48 d2.utils.events]:  eta: 0:25:32  iter: 8279  total_loss: 0.144  loss_cls: 0.073  loss_box_reg: 0.068  time: 0.2294  data_time: 0.0113  lr: 0.000001  max_mem: 10044M
[08/07 15:13:53 d2.utils.events]:  eta: 0:25:27  iter: 8299  total_loss: 0.130  loss_cls: 0.070  loss_box_reg: 0.063  time: 0.2294  data_time: 0.0126  lr: 0.000001  max_mem: 10044M
[08/07 15:13:57 d2.utils.events]:  eta: 0:25:22  iter: 8319  total_loss: 0.140  loss_cls: 0.074  loss_box_reg: 0.068  time: 0.2293  data_time: 0.0142  lr: 0.000001  max_mem: 10044M
[08/07 15:14:02 d2.utils.events]:  eta: 0:25:15  iter: 8339  total_loss: 0.122  loss_cls: 0.063  loss_box_reg: 0.061  time: 0.2293  data_time: 0.0111  lr: 0.000001  max_mem: 10044M
[08/07 15:14:06 d2.utils.events]:  eta: 0:25:14  iter: 8359  total_loss: 0.148  loss_cls: 0.076  loss_box_reg: 0.072  time: 0.2293  data_time: 0.0174  lr: 0.000001  max_mem: 10044M
[08/07 15:14:11 d2.utils.events]:  eta: 0:25:10  iter: 8379  total_loss: 0.123  loss_cls: 0.067  loss_box_reg: 0.061  time: 0.2293  data_time: 0.0121  lr: 0.000001  max_mem: 10044M
[08/07 15:14:16 d2.utils.events]:  eta: 0:25:06  iter: 8399  total_loss: 0.145  loss_cls: 0.074  loss_box_reg: 0.069  time: 0.2294  data_time: 0.0140  lr: 0.000001  max_mem: 10044M
[08/07 15:14:20 d2.utils.events]:  eta: 0:25:02  iter: 8419  total_loss: 0.136  loss_cls: 0.068  loss_box_reg: 0.068  time: 0.2294  data_time: 0.0161  lr: 0.000001  max_mem: 10044M
[08/07 15:14:25 d2.utils.events]:  eta: 0:24:56  iter: 8439  total_loss: 0.127  loss_cls: 0.067  loss_box_reg: 0.060  time: 0.2294  data_time: 0.0195  lr: 0.000001  max_mem: 10044M
[08/07 15:14:29 d2.utils.events]:  eta: 0:24:53  iter: 8459  total_loss: 0.154  loss_cls: 0.078  loss_box_reg: 0.077  time: 0.2294  data_time: 0.0127  lr: 0.000001  max_mem: 10044M
[08/07 15:14:34 d2.utils.events]:  eta: 0:24:47  iter: 8479  total_loss: 0.132  loss_cls: 0.067  loss_box_reg: 0.065  time: 0.2294  data_time: 0.0113  lr: 0.000001  max_mem: 10044M
[08/07 15:14:39 d2.utils.events]:  eta: 0:24:42  iter: 8499  total_loss: 0.140  loss_cls: 0.075  loss_box_reg: 0.068  time: 0.2293  data_time: 0.0113  lr: 0.000001  max_mem: 10044M
[08/07 15:14:43 d2.utils.events]:  eta: 0:24:37  iter: 8519  total_loss: 0.145  loss_cls: 0.075  loss_box_reg: 0.070  time: 0.2293  data_time: 0.0114  lr: 0.000001  max_mem: 10044M
[08/07 15:14:48 d2.utils.events]:  eta: 0:24:34  iter: 8539  total_loss: 0.141  loss_cls: 0.067  loss_box_reg: 0.071  time: 0.2293  data_time: 0.0117  lr: 0.000001  max_mem: 10044M
[08/07 15:14:52 d2.utils.events]:  eta: 0:24:30  iter: 8559  total_loss: 0.126  loss_cls: 0.067  loss_box_reg: 0.062  time: 0.2293  data_time: 0.0126  lr: 0.000001  max_mem: 10044M
[08/07 15:14:57 d2.utils.events]:  eta: 0:24:25  iter: 8579  total_loss: 0.154  loss_cls: 0.082  loss_box_reg: 0.072  time: 0.2294  data_time: 0.0133  lr: 0.000001  max_mem: 10044M
[08/07 15:15:01 d2.utils.events]:  eta: 0:24:21  iter: 8599  total_loss: 0.142  loss_cls: 0.072  loss_box_reg: 0.066  time: 0.2293  data_time: 0.0133  lr: 0.000001  max_mem: 10044M
[08/07 15:15:06 d2.utils.events]:  eta: 0:24:17  iter: 8619  total_loss: 0.139  loss_cls: 0.071  loss_box_reg: 0.069  time: 0.2293  data_time: 0.0146  lr: 0.000001  max_mem: 10044M
[08/07 15:15:11 d2.utils.events]:  eta: 0:24:12  iter: 8639  total_loss: 0.132  loss_cls: 0.068  loss_box_reg: 0.069  time: 0.2293  data_time: 0.0116  lr: 0.000001  max_mem: 10044M
[08/07 15:15:15 d2.utils.events]:  eta: 0:24:09  iter: 8659  total_loss: 0.147  loss_cls: 0.074  loss_box_reg: 0.073  time: 0.2293  data_time: 0.0125  lr: 0.000001  max_mem: 10044M
[08/07 15:15:20 d2.utils.events]:  eta: 0:24:04  iter: 8679  total_loss: 0.139  loss_cls: 0.072  loss_box_reg: 0.067  time: 0.2293  data_time: 0.0118  lr: 0.000001  max_mem: 10044M
[08/07 15:15:25 d2.utils.events]:  eta: 0:23:59  iter: 8699  total_loss: 0.132  loss_cls: 0.069  loss_box_reg: 0.060  time: 0.2294  data_time: 0.0150  lr: 0.000001  max_mem: 10044M
[08/07 15:15:30 d2.utils.events]:  eta: 0:23:56  iter: 8719  total_loss: 0.133  loss_cls: 0.065  loss_box_reg: 0.068  time: 0.2295  data_time: 0.0139  lr: 0.000001  max_mem: 10044M
[08/07 15:15:34 d2.utils.events]:  eta: 0:23:54  iter: 8739  total_loss: 0.143  loss_cls: 0.074  loss_box_reg: 0.069  time: 0.2294  data_time: 0.0129  lr: 0.000001  max_mem: 10044M
[08/07 15:15:39 d2.utils.events]:  eta: 0:23:50  iter: 8759  total_loss: 0.141  loss_cls: 0.073  loss_box_reg: 0.070  time: 0.2294  data_time: 0.0121  lr: 0.000001  max_mem: 10044M
[08/07 15:15:43 d2.utils.events]:  eta: 0:23:45  iter: 8779  total_loss: 0.134  loss_cls: 0.069  loss_box_reg: 0.067  time: 0.2294  data_time: 0.0112  lr: 0.000001  max_mem: 10044M
[08/07 15:15:48 d2.utils.events]:  eta: 0:23:42  iter: 8799  total_loss: 0.144  loss_cls: 0.074  loss_box_reg: 0.068  time: 0.2294  data_time: 0.0118  lr: 0.000001  max_mem: 10044M
[08/07 15:15:53 d2.utils.events]:  eta: 0:23:37  iter: 8819  total_loss: 0.132  loss_cls: 0.068  loss_box_reg: 0.064  time: 0.2294  data_time: 0.0115  lr: 0.000001  max_mem: 10044M
[08/07 15:15:57 d2.utils.events]:  eta: 0:23:34  iter: 8839  total_loss: 0.147  loss_cls: 0.073  loss_box_reg: 0.070  time: 0.2294  data_time: 0.0149  lr: 0.000001  max_mem: 10044M
[08/07 15:16:02 d2.utils.events]:  eta: 0:23:28  iter: 8859  total_loss: 0.138  loss_cls: 0.073  loss_box_reg: 0.067  time: 0.2294  data_time: 0.0149  lr: 0.000001  max_mem: 10044M
[08/07 15:16:06 d2.utils.events]:  eta: 0:23:24  iter: 8879  total_loss: 0.127  loss_cls: 0.066  loss_box_reg: 0.059  time: 0.2294  data_time: 0.0110  lr: 0.000001  max_mem: 10044M
[08/07 15:16:11 d2.utils.events]:  eta: 0:23:19  iter: 8899  total_loss: 0.144  loss_cls: 0.074  loss_box_reg: 0.072  time: 0.2294  data_time: 0.0130  lr: 0.000001  max_mem: 10044M
[08/07 15:16:15 d2.utils.events]:  eta: 0:23:13  iter: 8919  total_loss: 0.140  loss_cls: 0.072  loss_box_reg: 0.069  time: 0.2293  data_time: 0.0105  lr: 0.000001  max_mem: 10044M
[08/07 15:16:20 d2.utils.events]:  eta: 0:23:05  iter: 8939  total_loss: 0.139  loss_cls: 0.074  loss_box_reg: 0.065  time: 0.2293  data_time: 0.0123  lr: 0.000001  max_mem: 10044M
[08/07 15:16:24 d2.utils.events]:  eta: 0:23:04  iter: 8959  total_loss: 0.144  loss_cls: 0.074  loss_box_reg: 0.071  time: 0.2293  data_time: 0.0141  lr: 0.000001  max_mem: 10044M
[08/07 15:16:29 d2.utils.events]:  eta: 0:22:58  iter: 8979  total_loss: 0.140  loss_cls: 0.068  loss_box_reg: 0.071  time: 0.2293  data_time: 0.0149  lr: 0.000001  max_mem: 10044M
[08/07 15:16:33 d2.utils.events]:  eta: 0:22:54  iter: 8999  total_loss: 0.134  loss_cls: 0.072  loss_box_reg: 0.060  time: 0.2293  data_time: 0.0118  lr: 0.000001  max_mem: 10044M
[08/07 15:16:38 d2.utils.events]:  eta: 0:22:50  iter: 9019  total_loss: 0.137  loss_cls: 0.069  loss_box_reg: 0.068  time: 0.2293  data_time: 0.0150  lr: 0.000001  max_mem: 10044M
[08/07 15:16:42 d2.utils.events]:  eta: 0:22:43  iter: 9039  total_loss: 0.139  loss_cls: 0.073  loss_box_reg: 0.067  time: 0.2293  data_time: 0.0107  lr: 0.000001  max_mem: 10044M
[08/07 15:16:47 d2.utils.events]:  eta: 0:22:40  iter: 9059  total_loss: 0.142  loss_cls: 0.074  loss_box_reg: 0.067  time: 0.2292  data_time: 0.0147  lr: 0.000001  max_mem: 10044M
[08/07 15:16:52 d2.utils.events]:  eta: 0:22:37  iter: 9079  total_loss: 0.141  loss_cls: 0.074  loss_box_reg: 0.068  time: 0.2292  data_time: 0.0121  lr: 0.000001  max_mem: 10044M
[08/07 15:16:56 d2.utils.events]:  eta: 0:22:32  iter: 9099  total_loss: 0.131  loss_cls: 0.071  loss_box_reg: 0.060  time: 0.2293  data_time: 0.0108  lr: 0.000001  max_mem: 10044M
[08/07 15:17:01 d2.utils.events]:  eta: 0:22:26  iter: 9119  total_loss: 0.127  loss_cls: 0.065  loss_box_reg: 0.061  time: 0.2293  data_time: 0.0110  lr: 0.000001  max_mem: 10044M
[08/07 15:17:05 d2.utils.events]:  eta: 0:22:20  iter: 9139  total_loss: 0.140  loss_cls: 0.075  loss_box_reg: 0.066  time: 0.2292  data_time: 0.0119  lr: 0.000001  max_mem: 10044M
[08/07 15:17:10 d2.utils.events]:  eta: 0:22:14  iter: 9159  total_loss: 0.129  loss_cls: 0.064  loss_box_reg: 0.059  time: 0.2292  data_time: 0.0122  lr: 0.000001  max_mem: 10044M
[08/07 15:17:14 d2.utils.events]:  eta: 0:22:12  iter: 9179  total_loss: 0.138  loss_cls: 0.072  loss_box_reg: 0.067  time: 0.2292  data_time: 0.0118  lr: 0.000001  max_mem: 10044M
[08/07 15:17:19 d2.utils.events]:  eta: 0:22:09  iter: 9199  total_loss: 0.139  loss_cls: 0.072  loss_box_reg: 0.066  time: 0.2292  data_time: 0.0146  lr: 0.000001  max_mem: 10044M
[08/07 15:17:24 d2.utils.events]:  eta: 0:22:03  iter: 9219  total_loss: 0.143  loss_cls: 0.071  loss_box_reg: 0.070  time: 0.2292  data_time: 0.0122  lr: 0.000001  max_mem: 10044M
[08/07 15:17:28 d2.utils.events]:  eta: 0:21:57  iter: 9239  total_loss: 0.141  loss_cls: 0.075  loss_box_reg: 0.066  time: 0.2291  data_time: 0.0113  lr: 0.000001  max_mem: 10044M
[08/07 15:17:32 d2.utils.events]:  eta: 0:21:52  iter: 9259  total_loss: 0.145  loss_cls: 0.074  loss_box_reg: 0.069  time: 0.2291  data_time: 0.0108  lr: 0.000001  max_mem: 10044M
[08/07 15:17:37 d2.utils.events]:  eta: 0:21:46  iter: 9279  total_loss: 0.138  loss_cls: 0.073  loss_box_reg: 0.066  time: 0.2291  data_time: 0.0103  lr: 0.000001  max_mem: 10044M
[08/07 15:17:42 d2.utils.events]:  eta: 0:21:42  iter: 9299  total_loss: 0.160  loss_cls: 0.079  loss_box_reg: 0.077  time: 0.2291  data_time: 0.0134  lr: 0.000001  max_mem: 10044M
[08/07 15:17:46 d2.utils.events]:  eta: 0:21:36  iter: 9319  total_loss: 0.133  loss_cls: 0.068  loss_box_reg: 0.064  time: 0.2291  data_time: 0.0129  lr: 0.000001  max_mem: 10044M
[08/07 15:17:50 d2.utils.events]:  eta: 0:21:32  iter: 9339  total_loss: 0.143  loss_cls: 0.071  loss_box_reg: 0.069  time: 0.2290  data_time: 0.0119  lr: 0.000001  max_mem: 10044M
[08/07 15:17:55 d2.utils.events]:  eta: 0:21:29  iter: 9359  total_loss: 0.131  loss_cls: 0.066  loss_box_reg: 0.066  time: 0.2290  data_time: 0.0149  lr: 0.000001  max_mem: 10044M
[08/07 15:18:00 d2.utils.events]:  eta: 0:21:23  iter: 9379  total_loss: 0.140  loss_cls: 0.072  loss_box_reg: 0.066  time: 0.2290  data_time: 0.0117  lr: 0.000001  max_mem: 10044M
[08/07 15:18:04 d2.utils.events]:  eta: 0:21:18  iter: 9399  total_loss: 0.141  loss_cls: 0.075  loss_box_reg: 0.069  time: 0.2290  data_time: 0.0151  lr: 0.000001  max_mem: 10044M
[08/07 15:18:09 d2.utils.events]:  eta: 0:21:10  iter: 9419  total_loss: 0.136  loss_cls: 0.068  loss_box_reg: 0.066  time: 0.2290  data_time: 0.0122  lr: 0.000001  max_mem: 10044M
[08/07 15:18:13 d2.utils.events]:  eta: 0:21:05  iter: 9439  total_loss: 0.130  loss_cls: 0.067  loss_box_reg: 0.061  time: 0.2290  data_time: 0.0117  lr: 0.000001  max_mem: 10044M
[08/07 15:18:18 d2.utils.events]:  eta: 0:20:58  iter: 9459  total_loss: 0.129  loss_cls: 0.066  loss_box_reg: 0.065  time: 0.2290  data_time: 0.0114  lr: 0.000001  max_mem: 10044M
[08/07 15:18:22 d2.utils.events]:  eta: 0:20:56  iter: 9479  total_loss: 0.144  loss_cls: 0.074  loss_box_reg: 0.072  time: 0.2289  data_time: 0.0127  lr: 0.000001  max_mem: 10044M
[08/07 15:18:27 d2.utils.events]:  eta: 0:20:49  iter: 9499  total_loss: 0.131  loss_cls: 0.067  loss_box_reg: 0.063  time: 0.2289  data_time: 0.0108  lr: 0.000001  max_mem: 10044M
[08/07 15:18:31 d2.utils.events]:  eta: 0:20:46  iter: 9519  total_loss: 0.149  loss_cls: 0.072  loss_box_reg: 0.074  time: 0.2289  data_time: 0.0110  lr: 0.000001  max_mem: 10044M
[08/07 15:18:36 d2.utils.events]:  eta: 0:20:39  iter: 9539  total_loss: 0.141  loss_cls: 0.072  loss_box_reg: 0.066  time: 0.2289  data_time: 0.0138  lr: 0.000001  max_mem: 10044M
[08/07 15:18:40 d2.utils.events]:  eta: 0:20:33  iter: 9559  total_loss: 0.123  loss_cls: 0.064  loss_box_reg: 0.061  time: 0.2289  data_time: 0.0128  lr: 0.000001  max_mem: 10044M
[08/07 15:18:45 d2.utils.events]:  eta: 0:20:29  iter: 9579  total_loss: 0.125  loss_cls: 0.065  loss_box_reg: 0.062  time: 0.2289  data_time: 0.0141  lr: 0.000001  max_mem: 10044M
[08/07 15:18:50 d2.utils.events]:  eta: 0:20:25  iter: 9599  total_loss: 0.143  loss_cls: 0.073  loss_box_reg: 0.067  time: 0.2289  data_time: 0.0117  lr: 0.000001  max_mem: 10044M
[08/07 15:18:54 d2.utils.events]:  eta: 0:20:20  iter: 9619  total_loss: 0.131  loss_cls: 0.071  loss_box_reg: 0.066  time: 0.2289  data_time: 0.0117  lr: 0.000001  max_mem: 10044M
[08/07 15:18:59 d2.utils.events]:  eta: 0:20:16  iter: 9639  total_loss: 0.129  loss_cls: 0.068  loss_box_reg: 0.060  time: 0.2289  data_time: 0.0119  lr: 0.000001  max_mem: 10044M
[08/07 15:19:03 d2.utils.events]:  eta: 0:20:10  iter: 9659  total_loss: 0.134  loss_cls: 0.069  loss_box_reg: 0.063  time: 0.2289  data_time: 0.0131  lr: 0.000001  max_mem: 10044M
[08/07 15:19:08 d2.utils.events]:  eta: 0:20:05  iter: 9679  total_loss: 0.142  loss_cls: 0.074  loss_box_reg: 0.072  time: 0.2288  data_time: 0.0115  lr: 0.000001  max_mem: 10044M
[08/07 15:19:12 d2.utils.events]:  eta: 0:20:00  iter: 9699  total_loss: 0.132  loss_cls: 0.068  loss_box_reg: 0.064  time: 0.2288  data_time: 0.0109  lr: 0.000001  max_mem: 10044M
[08/07 15:19:17 d2.utils.events]:  eta: 0:19:53  iter: 9719  total_loss: 0.135  loss_cls: 0.071  loss_box_reg: 0.066  time: 0.2288  data_time: 0.0112  lr: 0.000001  max_mem: 10044M
[08/07 15:19:21 d2.utils.events]:  eta: 0:19:49  iter: 9739  total_loss: 0.138  loss_cls: 0.073  loss_box_reg: 0.065  time: 0.2288  data_time: 0.0122  lr: 0.000001  max_mem: 10044M
[08/07 15:19:26 d2.utils.events]:  eta: 0:19:45  iter: 9759  total_loss: 0.147  loss_cls: 0.080  loss_box_reg: 0.067  time: 0.2288  data_time: 0.0127  lr: 0.000001  max_mem: 10044M
[08/07 15:19:31 d2.utils.events]:  eta: 0:19:40  iter: 9779  total_loss: 0.153  loss_cls: 0.079  loss_box_reg: 0.074  time: 0.2288  data_time: 0.0139  lr: 0.000001  max_mem: 10044M
[08/07 15:19:35 d2.utils.events]:  eta: 0:19:35  iter: 9799  total_loss: 0.139  loss_cls: 0.068  loss_box_reg: 0.070  time: 0.2288  data_time: 0.0134  lr: 0.000001  max_mem: 10044M
[08/07 15:19:40 d2.utils.events]:  eta: 0:19:31  iter: 9819  total_loss: 0.141  loss_cls: 0.074  loss_box_reg: 0.070  time: 0.2288  data_time: 0.0146  lr: 0.000001  max_mem: 10044M
[08/07 15:19:44 d2.utils.events]:  eta: 0:19:26  iter: 9839  total_loss: 0.149  loss_cls: 0.076  loss_box_reg: 0.072  time: 0.2288  data_time: 0.0108  lr: 0.000001  max_mem: 10044M
[08/07 15:19:49 d2.utils.events]:  eta: 0:19:23  iter: 9859  total_loss: 0.136  loss_cls: 0.072  loss_box_reg: 0.067  time: 0.2288  data_time: 0.0118  lr: 0.000001  max_mem: 10044M
[08/07 15:19:54 d2.utils.events]:  eta: 0:19:19  iter: 9879  total_loss: 0.135  loss_cls: 0.073  loss_box_reg: 0.067  time: 0.2288  data_time: 0.0127  lr: 0.000001  max_mem: 10044M
[08/07 15:19:58 d2.utils.events]:  eta: 0:19:15  iter: 9899  total_loss: 0.148  loss_cls: 0.075  loss_box_reg: 0.075  time: 0.2288  data_time: 0.0140  lr: 0.000001  max_mem: 10044M
[08/07 15:20:03 d2.utils.events]:  eta: 0:19:12  iter: 9919  total_loss: 0.133  loss_cls: 0.066  loss_box_reg: 0.065  time: 0.2288  data_time: 0.0123  lr: 0.000001  max_mem: 10044M
[08/07 15:20:07 d2.utils.events]:  eta: 0:19:08  iter: 9939  total_loss: 0.145  loss_cls: 0.077  loss_box_reg: 0.069  time: 0.2288  data_time: 0.0105  lr: 0.000001  max_mem: 10044M
[08/07 15:20:12 d2.utils.events]:  eta: 0:19:04  iter: 9959  total_loss: 0.152  loss_cls: 0.074  loss_box_reg: 0.075  time: 0.2288  data_time: 0.0157  lr: 0.000001  max_mem: 10044M
[08/07 15:20:16 d2.utils.events]:  eta: 0:19:00  iter: 9979  total_loss: 0.145  loss_cls: 0.075  loss_box_reg: 0.069  time: 0.2288  data_time: 0.0134  lr: 0.000001  max_mem: 10044M
[08/07 15:20:23 d2.utils.events]:  eta: 0:18:56  iter: 9999  total_loss: 0.147  loss_cls: 0.074  loss_box_reg: 0.069  time: 0.2288  data_time: 0.0118  lr: 0.000001  max_mem: 10044M
[08/07 15:20:28 d2.utils.events]:  eta: 0:18:50  iter: 10019  total_loss: 0.150  loss_cls: 0.080  loss_box_reg: 0.073  time: 0.2287  data_time: 0.0117  lr: 0.000000  max_mem: 10044M
[08/07 15:20:33 d2.utils.events]:  eta: 0:18:45  iter: 10039  total_loss: 0.132  loss_cls: 0.073  loss_box_reg: 0.066  time: 0.2288  data_time: 0.0132  lr: 0.000000  max_mem: 10044M
[08/07 15:20:38 d2.utils.events]:  eta: 0:18:42  iter: 10059  total_loss: 0.132  loss_cls: 0.070  loss_box_reg: 0.064  time: 0.2289  data_time: 0.0163  lr: 0.000000  max_mem: 10044M
[08/07 15:20:42 d2.utils.events]:  eta: 0:18:37  iter: 10079  total_loss: 0.143  loss_cls: 0.075  loss_box_reg: 0.070  time: 0.2289  data_time: 0.0149  lr: 0.000000  max_mem: 10044M
[08/07 15:20:47 d2.utils.events]:  eta: 0:18:32  iter: 10099  total_loss: 0.147  loss_cls: 0.074  loss_box_reg: 0.072  time: 0.2289  data_time: 0.0135  lr: 0.000000  max_mem: 10044M
[08/07 15:20:52 d2.utils.events]:  eta: 0:18:29  iter: 10119  total_loss: 0.138  loss_cls: 0.071  loss_box_reg: 0.067  time: 0.2289  data_time: 0.0141  lr: 0.000000  max_mem: 10044M
[08/07 15:20:56 d2.utils.events]:  eta: 0:18:26  iter: 10139  total_loss: 0.134  loss_cls: 0.070  loss_box_reg: 0.065  time: 0.2289  data_time: 0.0125  lr: 0.000000  max_mem: 10044M
[08/07 15:21:01 d2.utils.events]:  eta: 0:18:22  iter: 10159  total_loss: 0.143  loss_cls: 0.076  loss_box_reg: 0.066  time: 0.2289  data_time: 0.0129  lr: 0.000000  max_mem: 10044M
[08/07 15:21:05 d2.utils.events]:  eta: 0:18:17  iter: 10179  total_loss: 0.141  loss_cls: 0.074  loss_box_reg: 0.068  time: 0.2289  data_time: 0.0120  lr: 0.000000  max_mem: 10044M
[08/07 15:21:10 d2.utils.events]:  eta: 0:18:12  iter: 10199  total_loss: 0.137  loss_cls: 0.070  loss_box_reg: 0.064  time: 0.2289  data_time: 0.0124  lr: 0.000000  max_mem: 10044M
[08/07 15:21:14 d2.utils.events]:  eta: 0:18:08  iter: 10219  total_loss: 0.134  loss_cls: 0.069  loss_box_reg: 0.065  time: 0.2289  data_time: 0.0144  lr: 0.000000  max_mem: 10044M
[08/07 15:21:19 d2.utils.events]:  eta: 0:18:04  iter: 10239  total_loss: 0.145  loss_cls: 0.073  loss_box_reg: 0.069  time: 0.2289  data_time: 0.0130  lr: 0.000000  max_mem: 10044M
[08/07 15:21:24 d2.utils.events]:  eta: 0:18:02  iter: 10259  total_loss: 0.109  loss_cls: 0.061  loss_box_reg: 0.052  time: 0.2289  data_time: 0.0133  lr: 0.000000  max_mem: 10044M
[08/07 15:21:28 d2.utils.events]:  eta: 0:17:58  iter: 10279  total_loss: 0.124  loss_cls: 0.067  loss_box_reg: 0.060  time: 0.2289  data_time: 0.0126  lr: 0.000000  max_mem: 10044M
[08/07 15:21:33 d2.utils.events]:  eta: 0:17:53  iter: 10299  total_loss: 0.134  loss_cls: 0.070  loss_box_reg: 0.062  time: 0.2288  data_time: 0.0093  lr: 0.000000  max_mem: 10044M
[08/07 15:21:37 d2.utils.events]:  eta: 0:17:46  iter: 10319  total_loss: 0.131  loss_cls: 0.071  loss_box_reg: 0.065  time: 0.2288  data_time: 0.0109  lr: 0.000000  max_mem: 10044M
[08/07 15:21:42 d2.utils.events]:  eta: 0:17:43  iter: 10339  total_loss: 0.143  loss_cls: 0.077  loss_box_reg: 0.065  time: 0.2288  data_time: 0.0134  lr: 0.000000  max_mem: 10044M
[08/07 15:21:46 d2.utils.events]:  eta: 0:17:36  iter: 10359  total_loss: 0.136  loss_cls: 0.071  loss_box_reg: 0.062  time: 0.2288  data_time: 0.0113  lr: 0.000000  max_mem: 10044M
[08/07 15:21:51 d2.utils.events]:  eta: 0:17:31  iter: 10379  total_loss: 0.147  loss_cls: 0.078  loss_box_reg: 0.067  time: 0.2288  data_time: 0.0124  lr: 0.000000  max_mem: 10044M
[08/07 15:21:55 d2.utils.events]:  eta: 0:17:27  iter: 10399  total_loss: 0.147  loss_cls: 0.076  loss_box_reg: 0.069  time: 0.2288  data_time: 0.0140  lr: 0.000000  max_mem: 10044M
[08/07 15:22:00 d2.utils.events]:  eta: 0:17:23  iter: 10419  total_loss: 0.134  loss_cls: 0.069  loss_box_reg: 0.068  time: 0.2288  data_time: 0.0129  lr: 0.000000  max_mem: 10044M
[08/07 15:22:04 d2.utils.events]:  eta: 0:17:19  iter: 10439  total_loss: 0.143  loss_cls: 0.073  loss_box_reg: 0.070  time: 0.2287  data_time: 0.0125  lr: 0.000000  max_mem: 10044M
[08/07 15:22:09 d2.utils.events]:  eta: 0:17:17  iter: 10459  total_loss: 0.142  loss_cls: 0.073  loss_box_reg: 0.073  time: 0.2288  data_time: 0.0142  lr: 0.000000  max_mem: 10044M
[08/07 15:22:14 d2.utils.events]:  eta: 0:17:13  iter: 10479  total_loss: 0.146  loss_cls: 0.075  loss_box_reg: 0.072  time: 0.2288  data_time: 0.0166  lr: 0.000000  max_mem: 10044M
[08/07 15:22:18 d2.utils.events]:  eta: 0:17:09  iter: 10499  total_loss: 0.124  loss_cls: 0.064  loss_box_reg: 0.058  time: 0.2288  data_time: 0.0130  lr: 0.000000  max_mem: 10044M
[08/07 15:22:23 d2.utils.events]:  eta: 0:17:05  iter: 10519  total_loss: 0.129  loss_cls: 0.069  loss_box_reg: 0.062  time: 0.2288  data_time: 0.0129  lr: 0.000000  max_mem: 10044M
[08/07 15:22:28 d2.utils.events]:  eta: 0:17:00  iter: 10539  total_loss: 0.144  loss_cls: 0.071  loss_box_reg: 0.068  time: 0.2288  data_time: 0.0125  lr: 0.000000  max_mem: 10044M
[08/07 15:22:32 d2.utils.events]:  eta: 0:16:57  iter: 10559  total_loss: 0.132  loss_cls: 0.065  loss_box_reg: 0.067  time: 0.2288  data_time: 0.0104  lr: 0.000000  max_mem: 10044M
[08/07 15:22:37 d2.utils.events]:  eta: 0:16:51  iter: 10579  total_loss: 0.136  loss_cls: 0.071  loss_box_reg: 0.066  time: 0.2288  data_time: 0.0132  lr: 0.000000  max_mem: 10044M
[08/07 15:22:41 d2.utils.events]:  eta: 0:16:45  iter: 10599  total_loss: 0.133  loss_cls: 0.070  loss_box_reg: 0.063  time: 0.2287  data_time: 0.0107  lr: 0.000000  max_mem: 10044M
[08/07 15:22:46 d2.utils.events]:  eta: 0:16:40  iter: 10619  total_loss: 0.140  loss_cls: 0.072  loss_box_reg: 0.067  time: 0.2287  data_time: 0.0114  lr: 0.000000  max_mem: 10044M
[08/07 15:22:50 d2.utils.events]:  eta: 0:16:35  iter: 10639  total_loss: 0.146  loss_cls: 0.073  loss_box_reg: 0.069  time: 0.2287  data_time: 0.0116  lr: 0.000000  max_mem: 10044M
[08/07 15:22:55 d2.utils.events]:  eta: 0:16:31  iter: 10659  total_loss: 0.138  loss_cls: 0.071  loss_box_reg: 0.064  time: 0.2287  data_time: 0.0133  lr: 0.000000  max_mem: 10044M
[08/07 15:22:59 d2.utils.events]:  eta: 0:16:27  iter: 10679  total_loss: 0.127  loss_cls: 0.067  loss_box_reg: 0.061  time: 0.2287  data_time: 0.0126  lr: 0.000000  max_mem: 10044M
[08/07 15:23:04 d2.utils.events]:  eta: 0:16:22  iter: 10699  total_loss: 0.127  loss_cls: 0.066  loss_box_reg: 0.061  time: 0.2287  data_time: 0.0140  lr: 0.000000  max_mem: 10044M
[08/07 15:23:08 d2.utils.events]:  eta: 0:16:19  iter: 10719  total_loss: 0.145  loss_cls: 0.073  loss_box_reg: 0.070  time: 0.2287  data_time: 0.0127  lr: 0.000000  max_mem: 10044M
[08/07 15:23:13 d2.utils.events]:  eta: 0:16:14  iter: 10739  total_loss: 0.137  loss_cls: 0.070  loss_box_reg: 0.067  time: 0.2287  data_time: 0.0115  lr: 0.000000  max_mem: 10044M
[08/07 15:23:17 d2.utils.events]:  eta: 0:16:10  iter: 10759  total_loss: 0.126  loss_cls: 0.063  loss_box_reg: 0.066  time: 0.2287  data_time: 0.0126  lr: 0.000000  max_mem: 10044M
[08/07 15:23:22 d2.utils.events]:  eta: 0:16:05  iter: 10779  total_loss: 0.147  loss_cls: 0.074  loss_box_reg: 0.070  time: 0.2287  data_time: 0.0132  lr: 0.000000  max_mem: 10044M
[08/07 15:23:26 d2.utils.events]:  eta: 0:16:01  iter: 10799  total_loss: 0.145  loss_cls: 0.074  loss_box_reg: 0.070  time: 0.2286  data_time: 0.0129  lr: 0.000000  max_mem: 10044M
[08/07 15:23:31 d2.utils.events]:  eta: 0:15:56  iter: 10819  total_loss: 0.156  loss_cls: 0.078  loss_box_reg: 0.075  time: 0.2286  data_time: 0.0142  lr: 0.000000  max_mem: 10044M
[08/07 15:23:35 d2.utils.events]:  eta: 0:15:52  iter: 10839  total_loss: 0.156  loss_cls: 0.079  loss_box_reg: 0.075  time: 0.2286  data_time: 0.0114  lr: 0.000000  max_mem: 10044M
[08/07 15:23:40 d2.utils.events]:  eta: 0:15:47  iter: 10859  total_loss: 0.141  loss_cls: 0.072  loss_box_reg: 0.067  time: 0.2286  data_time: 0.0128  lr: 0.000000  max_mem: 10044M
[08/07 15:23:44 d2.utils.events]:  eta: 0:15:41  iter: 10879  total_loss: 0.138  loss_cls: 0.073  loss_box_reg: 0.071  time: 0.2286  data_time: 0.0111  lr: 0.000000  max_mem: 10044M
[08/07 15:23:49 d2.utils.events]:  eta: 0:15:37  iter: 10899  total_loss: 0.141  loss_cls: 0.071  loss_box_reg: 0.068  time: 0.2285  data_time: 0.0124  lr: 0.000000  max_mem: 10044M
[08/07 15:23:53 d2.utils.events]:  eta: 0:15:33  iter: 10919  total_loss: 0.150  loss_cls: 0.074  loss_box_reg: 0.072  time: 0.2285  data_time: 0.0132  lr: 0.000000  max_mem: 10044M
[08/07 15:23:58 d2.utils.events]:  eta: 0:15:28  iter: 10939  total_loss: 0.147  loss_cls: 0.077  loss_box_reg: 0.070  time: 0.2286  data_time: 0.0137  lr: 0.000000  max_mem: 10044M
[08/07 15:24:03 d2.utils.events]:  eta: 0:15:23  iter: 10959  total_loss: 0.125  loss_cls: 0.065  loss_box_reg: 0.061  time: 0.2286  data_time: 0.0121  lr: 0.000000  max_mem: 10044M
[08/07 15:24:08 d2.utils.events]:  eta: 0:15:20  iter: 10979  total_loss: 0.156  loss_cls: 0.080  loss_box_reg: 0.078  time: 0.2286  data_time: 0.0141  lr: 0.000000  max_mem: 10044M
[08/07 15:24:12 d2.utils.events]:  eta: 0:15:14  iter: 10999  total_loss: 0.149  loss_cls: 0.074  loss_box_reg: 0.071  time: 0.2286  data_time: 0.0137  lr: 0.000000  max_mem: 10044M
[08/07 15:24:17 d2.utils.events]:  eta: 0:15:10  iter: 11019  total_loss: 0.143  loss_cls: 0.071  loss_box_reg: 0.065  time: 0.2286  data_time: 0.0135  lr: 0.000000  max_mem: 10044M
[08/07 15:24:21 d2.utils.events]:  eta: 0:15:06  iter: 11039  total_loss: 0.140  loss_cls: 0.074  loss_box_reg: 0.063  time: 0.2286  data_time: 0.0130  lr: 0.000000  max_mem: 10044M
[08/07 15:24:26 d2.utils.events]:  eta: 0:15:00  iter: 11059  total_loss: 0.128  loss_cls: 0.068  loss_box_reg: 0.063  time: 0.2287  data_time: 0.0130  lr: 0.000000  max_mem: 10044M
[08/07 15:24:31 d2.utils.events]:  eta: 0:14:56  iter: 11079  total_loss: 0.129  loss_cls: 0.069  loss_box_reg: 0.063  time: 0.2287  data_time: 0.0145  lr: 0.000000  max_mem: 10044M
[08/07 15:24:36 d2.utils.events]:  eta: 0:14:52  iter: 11099  total_loss: 0.126  loss_cls: 0.066  loss_box_reg: 0.061  time: 0.2287  data_time: 0.0137  lr: 0.000000  max_mem: 10044M
[08/07 15:24:40 d2.utils.events]:  eta: 0:14:47  iter: 11119  total_loss: 0.138  loss_cls: 0.072  loss_box_reg: 0.067  time: 0.2287  data_time: 0.0151  lr: 0.000000  max_mem: 10044M
[08/07 15:24:45 d2.utils.events]:  eta: 0:14:42  iter: 11139  total_loss: 0.145  loss_cls: 0.076  loss_box_reg: 0.067  time: 0.2287  data_time: 0.0122  lr: 0.000000  max_mem: 10044M
[08/07 15:24:50 d2.utils.events]:  eta: 0:14:37  iter: 11159  total_loss: 0.152  loss_cls: 0.076  loss_box_reg: 0.075  time: 0.2287  data_time: 0.0131  lr: 0.000000  max_mem: 10044M
[08/07 15:24:54 d2.utils.events]:  eta: 0:14:34  iter: 11179  total_loss: 0.140  loss_cls: 0.066  loss_box_reg: 0.069  time: 0.2288  data_time: 0.0141  lr: 0.000000  max_mem: 10044M
[08/07 15:24:59 d2.utils.events]:  eta: 0:14:29  iter: 11199  total_loss: 0.144  loss_cls: 0.070  loss_box_reg: 0.071  time: 0.2288  data_time: 0.0132  lr: 0.000000  max_mem: 10044M
[08/07 15:25:04 d2.utils.events]:  eta: 0:14:25  iter: 11219  total_loss: 0.141  loss_cls: 0.068  loss_box_reg: 0.070  time: 0.2288  data_time: 0.0139  lr: 0.000000  max_mem: 10044M
[08/07 15:25:08 d2.utils.events]:  eta: 0:14:20  iter: 11239  total_loss: 0.128  loss_cls: 0.065  loss_box_reg: 0.059  time: 0.2287  data_time: 0.0128  lr: 0.000000  max_mem: 10044M
[08/07 15:25:13 d2.utils.events]:  eta: 0:14:15  iter: 11259  total_loss: 0.151  loss_cls: 0.076  loss_box_reg: 0.075  time: 0.2287  data_time: 0.0127  lr: 0.000000  max_mem: 10044M
[08/07 15:25:17 d2.utils.events]:  eta: 0:14:11  iter: 11279  total_loss: 0.134  loss_cls: 0.069  loss_box_reg: 0.064  time: 0.2287  data_time: 0.0124  lr: 0.000000  max_mem: 10044M
[08/07 15:25:22 d2.utils.events]:  eta: 0:14:06  iter: 11299  total_loss: 0.133  loss_cls: 0.067  loss_box_reg: 0.063  time: 0.2287  data_time: 0.0131  lr: 0.000000  max_mem: 10044M
[08/07 15:25:26 d2.utils.events]:  eta: 0:14:03  iter: 11319  total_loss: 0.153  loss_cls: 0.076  loss_box_reg: 0.076  time: 0.2287  data_time: 0.0139  lr: 0.000000  max_mem: 10044M
[08/07 15:25:31 d2.utils.events]:  eta: 0:13:57  iter: 11339  total_loss: 0.146  loss_cls: 0.072  loss_box_reg: 0.067  time: 0.2287  data_time: 0.0161  lr: 0.000000  max_mem: 10044M
[08/07 15:25:35 d2.utils.events]:  eta: 0:13:53  iter: 11359  total_loss: 0.139  loss_cls: 0.072  loss_box_reg: 0.066  time: 0.2287  data_time: 0.0143  lr: 0.000000  max_mem: 10044M
[08/07 15:25:40 d2.utils.events]:  eta: 0:13:49  iter: 11379  total_loss: 0.141  loss_cls: 0.072  loss_box_reg: 0.069  time: 0.2287  data_time: 0.0120  lr: 0.000000  max_mem: 10044M
[08/07 15:25:45 d2.utils.events]:  eta: 0:13:45  iter: 11399  total_loss: 0.150  loss_cls: 0.076  loss_box_reg: 0.070  time: 0.2287  data_time: 0.0149  lr: 0.000000  max_mem: 10044M
[08/07 15:25:49 d2.utils.events]:  eta: 0:13:40  iter: 11419  total_loss: 0.132  loss_cls: 0.066  loss_box_reg: 0.064  time: 0.2287  data_time: 0.0132  lr: 0.000000  max_mem: 10044M
[08/07 15:25:53 d2.utils.events]:  eta: 0:13:34  iter: 11439  total_loss: 0.142  loss_cls: 0.071  loss_box_reg: 0.065  time: 0.2287  data_time: 0.0118  lr: 0.000000  max_mem: 10044M
[08/07 15:25:58 d2.utils.events]:  eta: 0:13:30  iter: 11459  total_loss: 0.147  loss_cls: 0.074  loss_box_reg: 0.070  time: 0.2287  data_time: 0.0143  lr: 0.000000  max_mem: 10044M
[08/07 15:26:03 d2.utils.events]:  eta: 0:13:25  iter: 11479  total_loss: 0.138  loss_cls: 0.072  loss_box_reg: 0.068  time: 0.2287  data_time: 0.0132  lr: 0.000000  max_mem: 10044M
[08/07 15:26:07 d2.utils.events]:  eta: 0:13:19  iter: 11499  total_loss: 0.138  loss_cls: 0.072  loss_box_reg: 0.064  time: 0.2286  data_time: 0.0123  lr: 0.000000  max_mem: 10044M
[08/07 15:26:12 d2.utils.events]:  eta: 0:13:14  iter: 11519  total_loss: 0.156  loss_cls: 0.077  loss_box_reg: 0.075  time: 0.2286  data_time: 0.0153  lr: 0.000000  max_mem: 10044M
[08/07 15:26:16 d2.utils.events]:  eta: 0:13:10  iter: 11539  total_loss: 0.130  loss_cls: 0.067  loss_box_reg: 0.061  time: 0.2286  data_time: 0.0114  lr: 0.000000  max_mem: 10044M
[08/07 15:26:21 d2.utils.events]:  eta: 0:13:05  iter: 11559  total_loss: 0.137  loss_cls: 0.072  loss_box_reg: 0.066  time: 0.2286  data_time: 0.0137  lr: 0.000000  max_mem: 10044M
[08/07 15:26:25 d2.utils.events]:  eta: 0:13:01  iter: 11579  total_loss: 0.144  loss_cls: 0.074  loss_box_reg: 0.071  time: 0.2286  data_time: 0.0133  lr: 0.000000  max_mem: 10044M
[08/07 15:26:30 d2.utils.events]:  eta: 0:12:57  iter: 11599  total_loss: 0.140  loss_cls: 0.073  loss_box_reg: 0.067  time: 0.2286  data_time: 0.0111  lr: 0.000000  max_mem: 10044M
[08/07 15:26:34 d2.utils.events]:  eta: 0:12:52  iter: 11619  total_loss: 0.129  loss_cls: 0.068  loss_box_reg: 0.061  time: 0.2286  data_time: 0.0129  lr: 0.000000  max_mem: 10044M
[08/07 15:26:39 d2.utils.events]:  eta: 0:12:47  iter: 11639  total_loss: 0.147  loss_cls: 0.074  loss_box_reg: 0.069  time: 0.2286  data_time: 0.0126  lr: 0.000000  max_mem: 10044M
[08/07 15:26:43 d2.utils.events]:  eta: 0:12:41  iter: 11659  total_loss: 0.134  loss_cls: 0.069  loss_box_reg: 0.064  time: 0.2286  data_time: 0.0132  lr: 0.000000  max_mem: 10044M
[08/07 15:26:48 d2.utils.events]:  eta: 0:12:36  iter: 11679  total_loss: 0.151  loss_cls: 0.078  loss_box_reg: 0.077  time: 0.2286  data_time: 0.0124  lr: 0.000000  max_mem: 10044M
[08/07 15:26:52 d2.utils.events]:  eta: 0:12:32  iter: 11699  total_loss: 0.143  loss_cls: 0.076  loss_box_reg: 0.067  time: 0.2285  data_time: 0.0128  lr: 0.000000  max_mem: 10044M
[08/07 15:26:57 d2.utils.events]:  eta: 0:12:27  iter: 11719  total_loss: 0.125  loss_cls: 0.068  loss_box_reg: 0.059  time: 0.2285  data_time: 0.0108  lr: 0.000000  max_mem: 10044M
[08/07 15:27:01 d2.utils.events]:  eta: 0:12:22  iter: 11739  total_loss: 0.141  loss_cls: 0.073  loss_box_reg: 0.069  time: 0.2285  data_time: 0.0129  lr: 0.000000  max_mem: 10044M
[08/07 15:27:06 d2.utils.events]:  eta: 0:12:18  iter: 11759  total_loss: 0.148  loss_cls: 0.079  loss_box_reg: 0.067  time: 0.2285  data_time: 0.0120  lr: 0.000000  max_mem: 10044M
[08/07 15:27:10 d2.utils.events]:  eta: 0:12:13  iter: 11779  total_loss: 0.129  loss_cls: 0.070  loss_box_reg: 0.064  time: 0.2285  data_time: 0.0118  lr: 0.000000  max_mem: 10044M
[08/07 15:27:14 d2.utils.events]:  eta: 0:12:08  iter: 11799  total_loss: 0.141  loss_cls: 0.073  loss_box_reg: 0.067  time: 0.2284  data_time: 0.0132  lr: 0.000000  max_mem: 10044M
[08/07 15:27:19 d2.utils.events]:  eta: 0:12:03  iter: 11819  total_loss: 0.132  loss_cls: 0.070  loss_box_reg: 0.062  time: 0.2284  data_time: 0.0129  lr: 0.000000  max_mem: 10044M
[08/07 15:27:24 d2.utils.events]:  eta: 0:11:59  iter: 11839  total_loss: 0.136  loss_cls: 0.070  loss_box_reg: 0.068  time: 0.2284  data_time: 0.0143  lr: 0.000000  max_mem: 10044M
[08/07 15:27:28 d2.utils.events]:  eta: 0:11:54  iter: 11859  total_loss: 0.134  loss_cls: 0.073  loss_box_reg: 0.063  time: 0.2284  data_time: 0.0111  lr: 0.000000  max_mem: 10044M
[08/07 15:27:33 d2.utils.events]:  eta: 0:11:50  iter: 11879  total_loss: 0.138  loss_cls: 0.072  loss_box_reg: 0.069  time: 0.2284  data_time: 0.0121  lr: 0.000000  max_mem: 10044M
[08/07 15:27:38 d2.utils.events]:  eta: 0:11:46  iter: 11899  total_loss: 0.139  loss_cls: 0.071  loss_box_reg: 0.066  time: 0.2285  data_time: 0.0122  lr: 0.000000  max_mem: 10044M
[08/07 15:27:43 d2.utils.events]:  eta: 0:11:43  iter: 11919  total_loss: 0.137  loss_cls: 0.070  loss_box_reg: 0.070  time: 0.2285  data_time: 0.0142  lr: 0.000000  max_mem: 10044M
[08/07 15:27:48 d2.utils.events]:  eta: 0:11:38  iter: 11939  total_loss: 0.137  loss_cls: 0.070  loss_box_reg: 0.065  time: 0.2286  data_time: 0.0127  lr: 0.000000  max_mem: 10044M
[08/07 15:27:52 d2.utils.events]:  eta: 0:11:34  iter: 11959  total_loss: 0.133  loss_cls: 0.070  loss_box_reg: 0.063  time: 0.2286  data_time: 0.0139  lr: 0.000000  max_mem: 10044M
[08/07 15:27:57 d2.utils.events]:  eta: 0:11:30  iter: 11979  total_loss: 0.146  loss_cls: 0.077  loss_box_reg: 0.072  time: 0.2286  data_time: 0.0144  lr: 0.000000  max_mem: 10044M
[08/07 15:28:02 d2.utils.events]:  eta: 0:11:26  iter: 11999  total_loss: 0.135  loss_cls: 0.068  loss_box_reg: 0.061  time: 0.2287  data_time: 0.0141  lr: 0.000000  max_mem: 10044M
[08/07 15:28:07 d2.utils.events]:  eta: 0:11:23  iter: 12019  total_loss: 0.142  loss_cls: 0.071  loss_box_reg: 0.071  time: 0.2288  data_time: 0.0161  lr: 0.000000  max_mem: 10044M
[08/07 15:28:12 d2.utils.events]:  eta: 0:11:19  iter: 12039  total_loss: 0.131  loss_cls: 0.070  loss_box_reg: 0.064  time: 0.2288  data_time: 0.0140  lr: 0.000000  max_mem: 10044M
[08/07 15:28:17 d2.utils.events]:  eta: 0:11:14  iter: 12059  total_loss: 0.144  loss_cls: 0.072  loss_box_reg: 0.070  time: 0.2288  data_time: 0.0115  lr: 0.000000  max_mem: 10044M
[08/07 15:28:22 d2.utils.events]:  eta: 0:11:11  iter: 12079  total_loss: 0.132  loss_cls: 0.069  loss_box_reg: 0.065  time: 0.2289  data_time: 0.0188  lr: 0.000000  max_mem: 10044M
[08/07 15:28:27 d2.utils.events]:  eta: 0:11:07  iter: 12099  total_loss: 0.150  loss_cls: 0.077  loss_box_reg: 0.073  time: 0.2289  data_time: 0.0134  lr: 0.000000  max_mem: 10044M
[08/07 15:28:32 d2.utils.events]:  eta: 0:11:02  iter: 12119  total_loss: 0.131  loss_cls: 0.070  loss_box_reg: 0.063  time: 0.2290  data_time: 0.0146  lr: 0.000000  max_mem: 10044M
[08/07 15:28:36 d2.utils.events]:  eta: 0:10:58  iter: 12139  total_loss: 0.135  loss_cls: 0.068  loss_box_reg: 0.068  time: 0.2290  data_time: 0.0148  lr: 0.000000  max_mem: 10044M
[08/07 15:28:41 d2.utils.events]:  eta: 0:10:53  iter: 12159  total_loss: 0.136  loss_cls: 0.073  loss_box_reg: 0.062  time: 0.2290  data_time: 0.0128  lr: 0.000000  max_mem: 10044M
[08/07 15:28:46 d2.utils.events]:  eta: 0:10:49  iter: 12179  total_loss: 0.146  loss_cls: 0.068  loss_box_reg: 0.075  time: 0.2290  data_time: 0.0133  lr: 0.000000  max_mem: 10044M
[08/07 15:28:50 d2.utils.events]:  eta: 0:10:44  iter: 12199  total_loss: 0.139  loss_cls: 0.073  loss_box_reg: 0.068  time: 0.2290  data_time: 0.0139  lr: 0.000000  max_mem: 10044M
[08/07 15:28:55 d2.utils.events]:  eta: 0:10:39  iter: 12219  total_loss: 0.142  loss_cls: 0.075  loss_box_reg: 0.071  time: 0.2290  data_time: 0.0114  lr: 0.000000  max_mem: 10044M
[08/07 15:29:00 d2.utils.events]:  eta: 0:10:35  iter: 12239  total_loss: 0.137  loss_cls: 0.069  loss_box_reg: 0.066  time: 0.2290  data_time: 0.0170  lr: 0.000000  max_mem: 10044M
[08/07 15:29:04 d2.utils.events]:  eta: 0:10:31  iter: 12259  total_loss: 0.150  loss_cls: 0.073  loss_box_reg: 0.075  time: 0.2290  data_time: 0.0130  lr: 0.000000  max_mem: 10044M
[08/07 15:29:09 d2.utils.events]:  eta: 0:10:26  iter: 12279  total_loss: 0.143  loss_cls: 0.072  loss_box_reg: 0.071  time: 0.2290  data_time: 0.0125  lr: 0.000000  max_mem: 10044M
[08/07 15:29:13 d2.utils.events]:  eta: 0:10:22  iter: 12299  total_loss: 0.137  loss_cls: 0.068  loss_box_reg: 0.071  time: 0.2290  data_time: 0.0171  lr: 0.000000  max_mem: 10044M
[08/07 15:29:18 d2.utils.events]:  eta: 0:10:17  iter: 12319  total_loss: 0.147  loss_cls: 0.073  loss_box_reg: 0.073  time: 0.2290  data_time: 0.0146  lr: 0.000000  max_mem: 10044M
[08/07 15:29:23 d2.utils.events]:  eta: 0:10:13  iter: 12339  total_loss: 0.138  loss_cls: 0.069  loss_box_reg: 0.068  time: 0.2290  data_time: 0.0122  lr: 0.000000  max_mem: 10044M
[08/07 15:29:27 d2.utils.events]:  eta: 0:10:08  iter: 12359  total_loss: 0.123  loss_cls: 0.065  loss_box_reg: 0.062  time: 0.2290  data_time: 0.0124  lr: 0.000000  max_mem: 10044M
[08/07 15:29:32 d2.utils.events]:  eta: 0:10:03  iter: 12379  total_loss: 0.139  loss_cls: 0.070  loss_box_reg: 0.070  time: 0.2290  data_time: 0.0118  lr: 0.000000  max_mem: 10044M
[08/07 15:29:36 d2.utils.events]:  eta: 0:09:58  iter: 12399  total_loss: 0.126  loss_cls: 0.065  loss_box_reg: 0.063  time: 0.2290  data_time: 0.0127  lr: 0.000000  max_mem: 10044M
[08/07 15:29:41 d2.utils.events]:  eta: 0:09:54  iter: 12419  total_loss: 0.153  loss_cls: 0.076  loss_box_reg: 0.073  time: 0.2290  data_time: 0.0114  lr: 0.000000  max_mem: 10044M
[08/07 15:29:45 d2.utils.events]:  eta: 0:09:50  iter: 12439  total_loss: 0.126  loss_cls: 0.065  loss_box_reg: 0.063  time: 0.2290  data_time: 0.0122  lr: 0.000000  max_mem: 10044M
[08/07 15:29:50 d2.utils.events]:  eta: 0:09:45  iter: 12459  total_loss: 0.134  loss_cls: 0.073  loss_box_reg: 0.063  time: 0.2290  data_time: 0.0138  lr: 0.000000  max_mem: 10044M
[08/07 15:29:55 d2.utils.events]:  eta: 0:09:41  iter: 12479  total_loss: 0.143  loss_cls: 0.070  loss_box_reg: 0.070  time: 0.2290  data_time: 0.0125  lr: 0.000000  max_mem: 10044M
[08/07 15:29:59 d2.utils.events]:  eta: 0:09:37  iter: 12499  total_loss: 0.147  loss_cls: 0.076  loss_box_reg: 0.071  time: 0.2290  data_time: 0.0108  lr: 0.000000  max_mem: 10044M
[08/07 15:30:04 d2.utils.events]:  eta: 0:09:33  iter: 12519  total_loss: 0.138  loss_cls: 0.071  loss_box_reg: 0.071  time: 0.2290  data_time: 0.0135  lr: 0.000000  max_mem: 10044M
[08/07 15:30:08 d2.utils.events]:  eta: 0:09:28  iter: 12539  total_loss: 0.136  loss_cls: 0.069  loss_box_reg: 0.063  time: 0.2290  data_time: 0.0105  lr: 0.000000  max_mem: 10044M
[08/07 15:30:13 d2.utils.events]:  eta: 0:09:24  iter: 12559  total_loss: 0.150  loss_cls: 0.074  loss_box_reg: 0.073  time: 0.2290  data_time: 0.0121  lr: 0.000000  max_mem: 10044M
[08/07 15:30:17 d2.utils.events]:  eta: 0:09:18  iter: 12579  total_loss: 0.134  loss_cls: 0.070  loss_box_reg: 0.064  time: 0.2290  data_time: 0.0128  lr: 0.000000  max_mem: 10044M
[08/07 15:30:22 d2.utils.events]:  eta: 0:09:14  iter: 12599  total_loss: 0.135  loss_cls: 0.072  loss_box_reg: 0.062  time: 0.2290  data_time: 0.0148  lr: 0.000000  max_mem: 10044M
[08/07 15:30:27 d2.utils.events]:  eta: 0:09:10  iter: 12619  total_loss: 0.137  loss_cls: 0.070  loss_box_reg: 0.067  time: 0.2290  data_time: 0.0115  lr: 0.000000  max_mem: 10044M
[08/07 15:30:31 d2.utils.events]:  eta: 0:09:07  iter: 12639  total_loss: 0.136  loss_cls: 0.073  loss_box_reg: 0.065  time: 0.2290  data_time: 0.0130  lr: 0.000000  max_mem: 10044M
[08/07 15:30:36 d2.utils.events]:  eta: 0:09:03  iter: 12659  total_loss: 0.145  loss_cls: 0.072  loss_box_reg: 0.069  time: 0.2290  data_time: 0.0148  lr: 0.000000  max_mem: 10044M
[08/07 15:30:41 d2.utils.events]:  eta: 0:09:00  iter: 12679  total_loss: 0.145  loss_cls: 0.074  loss_box_reg: 0.077  time: 0.2291  data_time: 0.0104  lr: 0.000000  max_mem: 10044M
[08/07 15:30:46 d2.utils.events]:  eta: 0:08:57  iter: 12699  total_loss: 0.124  loss_cls: 0.064  loss_box_reg: 0.061  time: 0.2291  data_time: 0.0125  lr: 0.000000  max_mem: 10044M
[08/07 15:30:51 d2.utils.events]:  eta: 0:08:54  iter: 12719  total_loss: 0.124  loss_cls: 0.063  loss_box_reg: 0.063  time: 0.2291  data_time: 0.0145  lr: 0.000000  max_mem: 10044M
[08/07 15:30:56 d2.utils.events]:  eta: 0:08:49  iter: 12739  total_loss: 0.152  loss_cls: 0.080  loss_box_reg: 0.072  time: 0.2292  data_time: 0.0132  lr: 0.000000  max_mem: 10044M
[08/07 15:31:01 d2.utils.events]:  eta: 0:08:44  iter: 12759  total_loss: 0.144  loss_cls: 0.072  loss_box_reg: 0.073  time: 0.2292  data_time: 0.0112  lr: 0.000000  max_mem: 10044M
[08/07 15:31:05 d2.utils.events]:  eta: 0:08:41  iter: 12779  total_loss: 0.124  loss_cls: 0.064  loss_box_reg: 0.060  time: 0.2292  data_time: 0.0143  lr: 0.000000  max_mem: 10044M
[08/07 15:31:10 d2.utils.events]:  eta: 0:08:37  iter: 12799  total_loss: 0.139  loss_cls: 0.072  loss_box_reg: 0.066  time: 0.2292  data_time: 0.0167  lr: 0.000000  max_mem: 10044M
[08/07 15:31:15 d2.utils.events]:  eta: 0:08:34  iter: 12819  total_loss: 0.134  loss_cls: 0.069  loss_box_reg: 0.066  time: 0.2293  data_time: 0.0167  lr: 0.000000  max_mem: 10044M
[08/07 15:31:20 d2.utils.events]:  eta: 0:08:29  iter: 12839  total_loss: 0.138  loss_cls: 0.073  loss_box_reg: 0.066  time: 0.2293  data_time: 0.0133  lr: 0.000000  max_mem: 10044M
[08/07 15:31:25 d2.utils.events]:  eta: 0:08:27  iter: 12859  total_loss: 0.137  loss_cls: 0.072  loss_box_reg: 0.066  time: 0.2293  data_time: 0.0120  lr: 0.000000  max_mem: 10044M
[08/07 15:31:30 d2.utils.events]:  eta: 0:08:23  iter: 12879  total_loss: 0.127  loss_cls: 0.067  loss_box_reg: 0.063  time: 0.2294  data_time: 0.0147  lr: 0.000000  max_mem: 10044M
[08/07 15:31:34 d2.utils.events]:  eta: 0:08:17  iter: 12899  total_loss: 0.144  loss_cls: 0.074  loss_box_reg: 0.067  time: 0.2294  data_time: 0.0114  lr: 0.000000  max_mem: 10044M
[08/07 15:31:39 d2.utils.events]:  eta: 0:08:11  iter: 12919  total_loss: 0.131  loss_cls: 0.070  loss_box_reg: 0.064  time: 0.2294  data_time: 0.0132  lr: 0.000000  max_mem: 10044M
[08/07 15:31:43 d2.utils.events]:  eta: 0:08:07  iter: 12939  total_loss: 0.128  loss_cls: 0.069  loss_box_reg: 0.060  time: 0.2294  data_time: 0.0127  lr: 0.000000  max_mem: 10044M
[08/07 15:31:48 d2.utils.events]:  eta: 0:08:01  iter: 12959  total_loss: 0.137  loss_cls: 0.073  loss_box_reg: 0.061  time: 0.2294  data_time: 0.0122  lr: 0.000000  max_mem: 10044M
[08/07 15:31:52 d2.utils.events]:  eta: 0:07:55  iter: 12979  total_loss: 0.137  loss_cls: 0.071  loss_box_reg: 0.068  time: 0.2293  data_time: 0.0127  lr: 0.000000  max_mem: 10044M
[08/07 15:31:57 d2.utils.events]:  eta: 0:07:51  iter: 12999  total_loss: 0.156  loss_cls: 0.084  loss_box_reg: 0.075  time: 0.2293  data_time: 0.0127  lr: 0.000000  max_mem: 10044M
[08/07 15:32:01 d2.utils.events]:  eta: 0:07:45  iter: 13019  total_loss: 0.154  loss_cls: 0.075  loss_box_reg: 0.071  time: 0.2293  data_time: 0.0137  lr: 0.000000  max_mem: 10044M
[08/07 15:32:06 d2.utils.events]:  eta: 0:07:39  iter: 13039  total_loss: 0.127  loss_cls: 0.066  loss_box_reg: 0.059  time: 0.2293  data_time: 0.0108  lr: 0.000000  max_mem: 10044M
[08/07 15:32:11 d2.utils.events]:  eta: 0:07:34  iter: 13059  total_loss: 0.146  loss_cls: 0.072  loss_box_reg: 0.071  time: 0.2293  data_time: 0.0125  lr: 0.000000  max_mem: 10044M
[08/07 15:32:15 d2.utils.events]:  eta: 0:07:28  iter: 13079  total_loss: 0.119  loss_cls: 0.064  loss_box_reg: 0.057  time: 0.2293  data_time: 0.0101  lr: 0.000000  max_mem: 10044M
[08/07 15:32:20 d2.utils.events]:  eta: 0:07:22  iter: 13099  total_loss: 0.130  loss_cls: 0.070  loss_box_reg: 0.063  time: 0.2293  data_time: 0.0143  lr: 0.000000  max_mem: 10044M
[08/07 15:32:24 d2.utils.events]:  eta: 0:07:16  iter: 13119  total_loss: 0.131  loss_cls: 0.067  loss_box_reg: 0.065  time: 0.2293  data_time: 0.0128  lr: 0.000000  max_mem: 10044M
[08/07 15:32:29 d2.utils.events]:  eta: 0:07:11  iter: 13139  total_loss: 0.136  loss_cls: 0.071  loss_box_reg: 0.065  time: 0.2293  data_time: 0.0131  lr: 0.000000  max_mem: 10044M
[08/07 15:32:34 d2.utils.events]:  eta: 0:07:06  iter: 13159  total_loss: 0.119  loss_cls: 0.066  loss_box_reg: 0.058  time: 0.2293  data_time: 0.0127  lr: 0.000000  max_mem: 10044M
[08/07 15:32:38 d2.utils.events]:  eta: 0:07:02  iter: 13179  total_loss: 0.131  loss_cls: 0.069  loss_box_reg: 0.062  time: 0.2293  data_time: 0.0163  lr: 0.000000  max_mem: 10044M
[08/07 15:32:43 d2.utils.events]:  eta: 0:06:57  iter: 13199  total_loss: 0.144  loss_cls: 0.077  loss_box_reg: 0.071  time: 0.2293  data_time: 0.0128  lr: 0.000000  max_mem: 10044M
[08/07 15:32:47 d2.utils.events]:  eta: 0:06:52  iter: 13219  total_loss: 0.132  loss_cls: 0.068  loss_box_reg: 0.065  time: 0.2293  data_time: 0.0142  lr: 0.000000  max_mem: 10044M
[08/07 15:32:52 d2.utils.events]:  eta: 0:06:48  iter: 13239  total_loss: 0.132  loss_cls: 0.071  loss_box_reg: 0.059  time: 0.2293  data_time: 0.0126  lr: 0.000000  max_mem: 10044M
[08/07 15:32:57 d2.utils.events]:  eta: 0:06:43  iter: 13259  total_loss: 0.135  loss_cls: 0.070  loss_box_reg: 0.066  time: 0.2293  data_time: 0.0135  lr: 0.000000  max_mem: 10044M
[08/07 15:33:01 d2.utils.events]:  eta: 0:06:38  iter: 13279  total_loss: 0.144  loss_cls: 0.072  loss_box_reg: 0.072  time: 0.2293  data_time: 0.0146  lr: 0.000000  max_mem: 10044M
[08/07 15:33:06 d2.utils.events]:  eta: 0:06:33  iter: 13299  total_loss: 0.130  loss_cls: 0.068  loss_box_reg: 0.065  time: 0.2293  data_time: 0.0147  lr: 0.000000  max_mem: 10044M
[08/07 15:33:11 d2.utils.events]:  eta: 0:06:29  iter: 13319  total_loss: 0.136  loss_cls: 0.066  loss_box_reg: 0.066  time: 0.2293  data_time: 0.0103  lr: 0.000000  max_mem: 10044M
[08/07 15:33:15 d2.utils.events]:  eta: 0:06:24  iter: 13339  total_loss: 0.135  loss_cls: 0.070  loss_box_reg: 0.064  time: 0.2293  data_time: 0.0129  lr: 0.000000  max_mem: 10044M
[08/07 15:33:20 d2.utils.events]:  eta: 0:06:20  iter: 13359  total_loss: 0.148  loss_cls: 0.077  loss_box_reg: 0.071  time: 0.2293  data_time: 0.0103  lr: 0.000000  max_mem: 10044M
[08/07 15:33:24 d2.utils.events]:  eta: 0:06:15  iter: 13379  total_loss: 0.146  loss_cls: 0.075  loss_box_reg: 0.070  time: 0.2293  data_time: 0.0151  lr: 0.000000  max_mem: 10044M
[08/07 15:33:29 d2.utils.events]:  eta: 0:06:11  iter: 13399  total_loss: 0.154  loss_cls: 0.079  loss_box_reg: 0.073  time: 0.2293  data_time: 0.0145  lr: 0.000000  max_mem: 10044M
[08/07 15:33:33 d2.utils.events]:  eta: 0:06:06  iter: 13419  total_loss: 0.133  loss_cls: 0.066  loss_box_reg: 0.062  time: 0.2293  data_time: 0.0129  lr: 0.000000  max_mem: 10044M
[08/07 15:33:38 d2.utils.events]:  eta: 0:06:01  iter: 13439  total_loss: 0.150  loss_cls: 0.080  loss_box_reg: 0.070  time: 0.2293  data_time: 0.0103  lr: 0.000000  max_mem: 10044M
[08/07 15:33:42 d2.utils.events]:  eta: 0:05:57  iter: 13459  total_loss: 0.141  loss_cls: 0.072  loss_box_reg: 0.071  time: 0.2293  data_time: 0.0125  lr: 0.000000  max_mem: 10044M
[08/07 15:33:47 d2.utils.events]:  eta: 0:05:53  iter: 13479  total_loss: 0.148  loss_cls: 0.073  loss_box_reg: 0.074  time: 0.2293  data_time: 0.0135  lr: 0.000000  max_mem: 10044M
[08/07 15:33:52 d2.utils.events]:  eta: 0:05:48  iter: 13499  total_loss: 0.137  loss_cls: 0.071  loss_box_reg: 0.065  time: 0.2293  data_time: 0.0116  lr: 0.000000  max_mem: 10044M
[08/07 15:33:56 d2.utils.events]:  eta: 0:05:43  iter: 13519  total_loss: 0.126  loss_cls: 0.066  loss_box_reg: 0.061  time: 0.2293  data_time: 0.0138  lr: 0.000000  max_mem: 10044M
[08/07 15:34:01 d2.utils.events]:  eta: 0:05:39  iter: 13539  total_loss: 0.129  loss_cls: 0.068  loss_box_reg: 0.060  time: 0.2293  data_time: 0.0127  lr: 0.000000  max_mem: 10044M
[08/07 15:34:06 d2.utils.events]:  eta: 0:05:34  iter: 13559  total_loss: 0.128  loss_cls: 0.065  loss_box_reg: 0.061  time: 0.2293  data_time: 0.0135  lr: 0.000000  max_mem: 10044M
[08/07 15:34:10 d2.utils.events]:  eta: 0:05:30  iter: 13579  total_loss: 0.145  loss_cls: 0.075  loss_box_reg: 0.070  time: 0.2293  data_time: 0.0121  lr: 0.000000  max_mem: 10044M
[08/07 15:34:15 d2.utils.events]:  eta: 0:05:26  iter: 13599  total_loss: 0.140  loss_cls: 0.073  loss_box_reg: 0.065  time: 0.2293  data_time: 0.0115  lr: 0.000000  max_mem: 10044M
[08/07 15:34:20 d2.utils.events]:  eta: 0:05:20  iter: 13619  total_loss: 0.154  loss_cls: 0.081  loss_box_reg: 0.072  time: 0.2293  data_time: 0.0129  lr: 0.000000  max_mem: 10044M
[08/07 15:34:24 d2.utils.events]:  eta: 0:05:15  iter: 13639  total_loss: 0.139  loss_cls: 0.072  loss_box_reg: 0.072  time: 0.2293  data_time: 0.0167  lr: 0.000000  max_mem: 10044M
[08/07 15:34:29 d2.utils.events]:  eta: 0:05:11  iter: 13659  total_loss: 0.135  loss_cls: 0.067  loss_box_reg: 0.065  time: 0.2293  data_time: 0.0137  lr: 0.000000  max_mem: 10044M
[08/07 15:34:33 d2.utils.events]:  eta: 0:05:06  iter: 13679  total_loss: 0.147  loss_cls: 0.073  loss_box_reg: 0.071  time: 0.2293  data_time: 0.0154  lr: 0.000000  max_mem: 10044M
[08/07 15:34:38 d2.utils.events]:  eta: 0:05:01  iter: 13699  total_loss: 0.148  loss_cls: 0.074  loss_box_reg: 0.069  time: 0.2293  data_time: 0.0132  lr: 0.000000  max_mem: 10044M
[08/07 15:34:43 d2.utils.events]:  eta: 0:04:56  iter: 13719  total_loss: 0.141  loss_cls: 0.071  loss_box_reg: 0.065  time: 0.2293  data_time: 0.0150  lr: 0.000000  max_mem: 10044M
[08/07 15:34:47 d2.utils.events]:  eta: 0:04:51  iter: 13739  total_loss: 0.136  loss_cls: 0.067  loss_box_reg: 0.067  time: 0.2293  data_time: 0.0136  lr: 0.000000  max_mem: 10044M
[08/07 15:34:52 d2.utils.events]:  eta: 0:04:46  iter: 13759  total_loss: 0.139  loss_cls: 0.073  loss_box_reg: 0.068  time: 0.2293  data_time: 0.0126  lr: 0.000000  max_mem: 10044M
[08/07 15:34:56 d2.utils.events]:  eta: 0:04:41  iter: 13779  total_loss: 0.128  loss_cls: 0.066  loss_box_reg: 0.061  time: 0.2293  data_time: 0.0151  lr: 0.000000  max_mem: 10044M
[08/07 15:35:01 d2.utils.events]:  eta: 0:04:36  iter: 13799  total_loss: 0.134  loss_cls: 0.073  loss_box_reg: 0.065  time: 0.2293  data_time: 0.0136  lr: 0.000000  max_mem: 10044M
[08/07 15:35:06 d2.utils.events]:  eta: 0:04:31  iter: 13819  total_loss: 0.136  loss_cls: 0.069  loss_box_reg: 0.067  time: 0.2293  data_time: 0.0127  lr: 0.000000  max_mem: 10044M
[08/07 15:35:10 d2.utils.events]:  eta: 0:04:26  iter: 13839  total_loss: 0.145  loss_cls: 0.072  loss_box_reg: 0.070  time: 0.2293  data_time: 0.0124  lr: 0.000000  max_mem: 10044M
[08/07 15:35:15 d2.utils.events]:  eta: 0:04:22  iter: 13859  total_loss: 0.129  loss_cls: 0.067  loss_box_reg: 0.064  time: 0.2293  data_time: 0.0137  lr: 0.000000  max_mem: 10044M
[08/07 15:35:20 d2.utils.events]:  eta: 0:04:16  iter: 13879  total_loss: 0.152  loss_cls: 0.076  loss_box_reg: 0.073  time: 0.2293  data_time: 0.0100  lr: 0.000000  max_mem: 10044M
[08/07 15:35:24 d2.utils.events]:  eta: 0:04:12  iter: 13899  total_loss: 0.159  loss_cls: 0.079  loss_box_reg: 0.074  time: 0.2294  data_time: 0.0133  lr: 0.000000  max_mem: 10044M
[08/07 15:35:29 d2.utils.events]:  eta: 0:04:07  iter: 13919  total_loss: 0.140  loss_cls: 0.073  loss_box_reg: 0.069  time: 0.2294  data_time: 0.0154  lr: 0.000000  max_mem: 10044M
[08/07 15:35:34 d2.utils.events]:  eta: 0:04:03  iter: 13939  total_loss: 0.153  loss_cls: 0.075  loss_box_reg: 0.078  time: 0.2294  data_time: 0.0129  lr: 0.000000  max_mem: 10044M
[08/07 15:35:38 d2.utils.events]:  eta: 0:03:59  iter: 13959  total_loss: 0.135  loss_cls: 0.069  loss_box_reg: 0.067  time: 0.2294  data_time: 0.0154  lr: 0.000000  max_mem: 10044M
[08/07 15:35:43 d2.utils.events]:  eta: 0:03:54  iter: 13979  total_loss: 0.134  loss_cls: 0.068  loss_box_reg: 0.068  time: 0.2294  data_time: 0.0145  lr: 0.000000  max_mem: 10044M
[08/07 15:35:48 d2.utils.events]:  eta: 0:03:50  iter: 13999  total_loss: 0.138  loss_cls: 0.071  loss_box_reg: 0.067  time: 0.2294  data_time: 0.0137  lr: 0.000000  max_mem: 10044M
[08/07 15:35:53 d2.utils.events]:  eta: 0:03:46  iter: 14019  total_loss: 0.131  loss_cls: 0.068  loss_box_reg: 0.063  time: 0.2294  data_time: 0.0141  lr: 0.000000  max_mem: 10044M
[08/07 15:35:57 d2.utils.events]:  eta: 0:03:41  iter: 14039  total_loss: 0.131  loss_cls: 0.070  loss_box_reg: 0.059  time: 0.2295  data_time: 0.0154  lr: 0.000000  max_mem: 10044M
[08/07 15:36:02 d2.utils.events]:  eta: 0:03:37  iter: 14059  total_loss: 0.142  loss_cls: 0.070  loss_box_reg: 0.067  time: 0.2295  data_time: 0.0124  lr: 0.000000  max_mem: 10044M
[08/07 15:36:07 d2.utils.events]:  eta: 0:03:32  iter: 14079  total_loss: 0.134  loss_cls: 0.071  loss_box_reg: 0.066  time: 0.2295  data_time: 0.0141  lr: 0.000000  max_mem: 10044M
[08/07 15:36:11 d2.utils.events]:  eta: 0:03:28  iter: 14099  total_loss: 0.145  loss_cls: 0.070  loss_box_reg: 0.074  time: 0.2295  data_time: 0.0133  lr: 0.000000  max_mem: 10044M
[08/07 15:36:16 d2.utils.events]:  eta: 0:03:23  iter: 14119  total_loss: 0.144  loss_cls: 0.072  loss_box_reg: 0.069  time: 0.2295  data_time: 0.0112  lr: 0.000000  max_mem: 10044M
[08/07 15:36:20 d2.utils.events]:  eta: 0:03:18  iter: 14139  total_loss: 0.144  loss_cls: 0.074  loss_box_reg: 0.070  time: 0.2294  data_time: 0.0113  lr: 0.000000  max_mem: 10044M
[08/07 15:36:25 d2.utils.events]:  eta: 0:03:14  iter: 14159  total_loss: 0.143  loss_cls: 0.071  loss_box_reg: 0.070  time: 0.2295  data_time: 0.0115  lr: 0.000000  max_mem: 10044M
[08/07 15:36:30 d2.utils.events]:  eta: 0:03:09  iter: 14179  total_loss: 0.123  loss_cls: 0.066  loss_box_reg: 0.059  time: 0.2294  data_time: 0.0160  lr: 0.000000  max_mem: 10044M
[08/07 15:36:34 d2.utils.events]:  eta: 0:03:05  iter: 14199  total_loss: 0.147  loss_cls: 0.076  loss_box_reg: 0.073  time: 0.2294  data_time: 0.0126  lr: 0.000000  max_mem: 10044M
[08/07 15:36:39 d2.utils.events]:  eta: 0:03:00  iter: 14219  total_loss: 0.161  loss_cls: 0.081  loss_box_reg: 0.076  time: 0.2294  data_time: 0.0113  lr: 0.000000  max_mem: 10044M
[08/07 15:36:43 d2.utils.events]:  eta: 0:02:55  iter: 14239  total_loss: 0.144  loss_cls: 0.076  loss_box_reg: 0.071  time: 0.2294  data_time: 0.0121  lr: 0.000000  max_mem: 10044M
[08/07 15:36:48 d2.utils.events]:  eta: 0:02:50  iter: 14259  total_loss: 0.141  loss_cls: 0.074  loss_box_reg: 0.068  time: 0.2294  data_time: 0.0113  lr: 0.000000  max_mem: 10044M
[08/07 15:36:52 d2.utils.events]:  eta: 0:02:45  iter: 14279  total_loss: 0.136  loss_cls: 0.067  loss_box_reg: 0.065  time: 0.2294  data_time: 0.0161  lr: 0.000000  max_mem: 10044M
[08/07 15:36:56 d2.utils.events]:  eta: 0:02:41  iter: 14299  total_loss: 0.150  loss_cls: 0.078  loss_box_reg: 0.071  time: 0.2294  data_time: 0.0128  lr: 0.000000  max_mem: 10044M
[08/07 15:37:01 d2.utils.events]:  eta: 0:02:36  iter: 14319  total_loss: 0.131  loss_cls: 0.070  loss_box_reg: 0.065  time: 0.2293  data_time: 0.0127  lr: 0.000000  max_mem: 10044M
[08/07 15:37:05 d2.utils.events]:  eta: 0:02:31  iter: 14339  total_loss: 0.145  loss_cls: 0.072  loss_box_reg: 0.070  time: 0.2293  data_time: 0.0091  lr: 0.000000  max_mem: 10044M
[08/07 15:37:10 d2.utils.events]:  eta: 0:02:27  iter: 14359  total_loss: 0.140  loss_cls: 0.073  loss_box_reg: 0.068  time: 0.2293  data_time: 0.0104  lr: 0.000000  max_mem: 10044M
[08/07 15:37:14 d2.utils.events]:  eta: 0:02:22  iter: 14379  total_loss: 0.134  loss_cls: 0.069  loss_box_reg: 0.069  time: 0.2293  data_time: 0.0131  lr: 0.000000  max_mem: 10044M
[08/07 15:37:18 d2.utils.events]:  eta: 0:02:17  iter: 14399  total_loss: 0.133  loss_cls: 0.069  loss_box_reg: 0.065  time: 0.2292  data_time: 0.0119  lr: 0.000000  max_mem: 10044M
[08/07 15:37:23 d2.utils.events]:  eta: 0:02:12  iter: 14419  total_loss: 0.145  loss_cls: 0.075  loss_box_reg: 0.070  time: 0.2292  data_time: 0.0141  lr: 0.000000  max_mem: 10044M
[08/07 15:37:27 d2.utils.events]:  eta: 0:02:08  iter: 14439  total_loss: 0.145  loss_cls: 0.075  loss_box_reg: 0.071  time: 0.2292  data_time: 0.0119  lr: 0.000000  max_mem: 10044M
[08/07 15:37:32 d2.utils.events]:  eta: 0:02:03  iter: 14459  total_loss: 0.140  loss_cls: 0.073  loss_box_reg: 0.065  time: 0.2292  data_time: 0.0119  lr: 0.000000  max_mem: 10044M
[08/07 15:37:36 d2.utils.events]:  eta: 0:01:58  iter: 14479  total_loss: 0.137  loss_cls: 0.069  loss_box_reg: 0.067  time: 0.2292  data_time: 0.0114  lr: 0.000000  max_mem: 10044M
[08/07 15:37:41 d2.utils.events]:  eta: 0:01:54  iter: 14499  total_loss: 0.139  loss_cls: 0.070  loss_box_reg: 0.065  time: 0.2292  data_time: 0.0105  lr: 0.000000  max_mem: 10044M
[08/07 15:37:45 d2.utils.events]:  eta: 0:01:49  iter: 14519  total_loss: 0.133  loss_cls: 0.067  loss_box_reg: 0.066  time: 0.2292  data_time: 0.0137  lr: 0.000000  max_mem: 10044M
[08/07 15:37:50 d2.utils.events]:  eta: 0:01:45  iter: 14539  total_loss: 0.139  loss_cls: 0.069  loss_box_reg: 0.067  time: 0.2291  data_time: 0.0136  lr: 0.000000  max_mem: 10044M
[08/07 15:37:54 d2.utils.events]:  eta: 0:01:40  iter: 14559  total_loss: 0.153  loss_cls: 0.078  loss_box_reg: 0.076  time: 0.2291  data_time: 0.0122  lr: 0.000000  max_mem: 10044M
[08/07 15:37:59 d2.utils.events]:  eta: 0:01:35  iter: 14579  total_loss: 0.146  loss_cls: 0.071  loss_box_reg: 0.073  time: 0.2291  data_time: 0.0112  lr: 0.000000  max_mem: 10044M
[08/07 15:38:03 d2.utils.events]:  eta: 0:01:31  iter: 14599  total_loss: 0.131  loss_cls: 0.070  loss_box_reg: 0.066  time: 0.2291  data_time: 0.0138  lr: 0.000000  max_mem: 10044M
[08/07 15:38:08 d2.utils.events]:  eta: 0:01:26  iter: 14619  total_loss: 0.149  loss_cls: 0.075  loss_box_reg: 0.075  time: 0.2291  data_time: 0.0116  lr: 0.000000  max_mem: 10044M
[08/07 15:38:12 d2.utils.events]:  eta: 0:01:22  iter: 14639  total_loss: 0.139  loss_cls: 0.073  loss_box_reg: 0.066  time: 0.2291  data_time: 0.0105  lr: 0.000000  max_mem: 10044M
[08/07 15:38:17 d2.utils.events]:  eta: 0:01:17  iter: 14659  total_loss: 0.148  loss_cls: 0.074  loss_box_reg: 0.071  time: 0.2291  data_time: 0.0139  lr: 0.000000  max_mem: 10044M
[08/07 15:38:22 d2.utils.events]:  eta: 0:01:13  iter: 14679  total_loss: 0.120  loss_cls: 0.064  loss_box_reg: 0.059  time: 0.2291  data_time: 0.0119  lr: 0.000000  max_mem: 10044M
[08/07 15:38:26 d2.utils.events]:  eta: 0:01:08  iter: 14699  total_loss: 0.128  loss_cls: 0.066  loss_box_reg: 0.063  time: 0.2291  data_time: 0.0140  lr: 0.000000  max_mem: 10044M
[08/07 15:38:31 d2.utils.events]:  eta: 0:01:04  iter: 14719  total_loss: 0.134  loss_cls: 0.071  loss_box_reg: 0.066  time: 0.2292  data_time: 0.0146  lr: 0.000000  max_mem: 10044M
[08/07 15:38:36 d2.utils.events]:  eta: 0:00:59  iter: 14739  total_loss: 0.131  loss_cls: 0.068  loss_box_reg: 0.064  time: 0.2292  data_time: 0.0129  lr: 0.000000  max_mem: 10044M
[08/07 15:38:40 d2.utils.events]:  eta: 0:00:54  iter: 14759  total_loss: 0.134  loss_cls: 0.069  loss_box_reg: 0.065  time: 0.2291  data_time: 0.0124  lr: 0.000000  max_mem: 10044M
[08/07 15:38:45 d2.utils.events]:  eta: 0:00:50  iter: 14779  total_loss: 0.138  loss_cls: 0.069  loss_box_reg: 0.069  time: 0.2291  data_time: 0.0127  lr: 0.000000  max_mem: 10044M
[08/07 15:38:50 d2.utils.events]:  eta: 0:00:45  iter: 14799  total_loss: 0.128  loss_cls: 0.070  loss_box_reg: 0.061  time: 0.2292  data_time: 0.0123  lr: 0.000000  max_mem: 10044M
[08/07 15:38:55 d2.utils.events]:  eta: 0:00:41  iter: 14819  total_loss: 0.150  loss_cls: 0.078  loss_box_reg: 0.074  time: 0.2292  data_time: 0.0136  lr: 0.000000  max_mem: 10044M
[08/07 15:39:00 d2.utils.events]:  eta: 0:00:36  iter: 14839  total_loss: 0.128  loss_cls: 0.065  loss_box_reg: 0.063  time: 0.2292  data_time: 0.0146  lr: 0.000000  max_mem: 10044M
[08/07 15:39:04 d2.utils.events]:  eta: 0:00:32  iter: 14859  total_loss: 0.140  loss_cls: 0.069  loss_box_reg: 0.070  time: 0.2292  data_time: 0.0144  lr: 0.000000  max_mem: 10044M
[08/07 15:39:09 d2.utils.events]:  eta: 0:00:27  iter: 14879  total_loss: 0.135  loss_cls: 0.068  loss_box_reg: 0.064  time: 0.2293  data_time: 0.0142  lr: 0.000000  max_mem: 10044M
[08/07 15:39:14 d2.utils.events]:  eta: 0:00:23  iter: 14899  total_loss: 0.149  loss_cls: 0.077  loss_box_reg: 0.070  time: 0.2293  data_time: 0.0122  lr: 0.000000  max_mem: 10044M
[08/07 15:39:18 d2.utils.events]:  eta: 0:00:18  iter: 14919  total_loss: 0.140  loss_cls: 0.073  loss_box_reg: 0.067  time: 0.2293  data_time: 0.0141  lr: 0.000000  max_mem: 10044M
[08/07 15:39:23 d2.utils.events]:  eta: 0:00:13  iter: 14939  total_loss: 0.141  loss_cls: 0.075  loss_box_reg: 0.066  time: 0.2293  data_time: 0.0133  lr: 0.000000  max_mem: 10044M
[08/07 15:39:28 d2.utils.events]:  eta: 0:00:09  iter: 14959  total_loss: 0.139  loss_cls: 0.071  loss_box_reg: 0.068  time: 0.2293  data_time: 0.0123  lr: 0.000000  max_mem: 10044M
[08/07 15:39:32 d2.utils.events]:  eta: 0:00:04  iter: 14979  total_loss: 0.139  loss_cls: 0.077  loss_box_reg: 0.070  time: 0.2293  data_time: 0.0119  lr: 0.000000  max_mem: 10044M
[08/07 15:39:42 d2.utils.events]:  eta: 0:00:00  iter: 14999  total_loss: 0.140  loss_cls: 0.071  loss_box_reg: 0.067  time: 0.2293  data_time: 0.0113  lr: 0.000000  max_mem: 10044M
[08/07 15:39:42 d2.engine.hooks]: Overall training speed: 9997 iterations in 0:38:12 (0.2293 s / it)
[08/07 15:39:42 d2.engine.hooks]: Total training time: 0:38:28 (0:00:15 on hooks)
In [ ]:
def cfg_test():
    cfg = get_cfg()
    cfg.merge_from_file(model_zoo.get_config_file(MODEL_PATH))
    cfg.MODEL.WEIGHTS = f'{OUTPUT_PATH}/model_final.pth'
    cfg.DATASETS.TEST = ('wheat_test',)
    cfg.MODEL.RETINANET.NUM_CLASSES = 1
    cfg.MODEL.RETINANET.SCORE_THRESH_TEST = 0.4
    return cfg

cfg = cfg_test()
predict = DefaultPredictor(cfg)

Results - Visualization

In [64]:
df_sub = pd.read_csv('/content/drive/My Drive/CV/Global Wheat Detection/sample_submission.csv')
TEST_DIR = '/content/drive/My Drive/CV/Global Wheat Detection/test'

fig, ax = plt.subplots(2, 5, figsize=(30, 17))
subplot_indexes = [(x,y) for x in range(2) for y in range(5)]
for index, image_id in enumerate(df_sub['image_id']):
    im = cv2.imread('{}/{}.jpg'.format(TEST_DIR, image_id))
    boxes = []
    scores = []
    labels = []
    outputs = predict(im)
    out = outputs["instances"].to("cpu")
    scores = out.get_fields()['scores'].numpy()
    boxes = out.get_fields()['pred_boxes'].tensor.numpy().astype(int)
    labels= out.get_fields()['scores'].numpy()
    boxes = boxes.astype(int)
    boxes[:, 2] = boxes[:, 2] - boxes[:, 0]
    boxes[:, 3] = boxes[:, 3] - boxes[:, 1]
    im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB).astype(np.float32)
    im /= 255.0
    
    for b,s in zip(boxes,scores):
        cv2.rectangle(im, (b[0],b[1]), (b[0]+b[2],b[1]+b[3]), (1,1,1), 3)
        cv2.putText(im, '{:.2}'.format(s), (b[0],b[1]), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (1,1,1), 2)
    ax[subplot_indexes[index]].set_axis_off()
    ax[subplot_indexes[index]].imshow(im)

fig.tight_layout()
fig.show()

Conclusions

I got a low score on the leader boards with this model, I was very disappointed with the models low performance. I searched for performance boosting for the detectron2 but I haven't found any sources(maybe because its still new), I tried tuning the parameters, tried several models, but in the end the model's score wasn't getting any higher. I believe that I could boost its performance by adding more custom augmentations or change the LR scheduler, but because of lack of time I haven't done any extra work. On the future I will get the most of this model (maybe on different competition :)).

In [ ]: